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+### Dataset Link : https://www.kaggle.com/datasets/shayanfazeli/heartbeat
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diff --git a/Heartbeat Classification using ECG/Model/README.md b/Heartbeat Classification using ECG/Model/README.md
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+# Heartbeat Classification using ECG
+
+### Goal
+The goal of this project is to create a deep learning model which will predict the heartbeat of the patients from the ECG images.
+
+### Dataset
+The dataset is collected from the following link, https://www.kaggle.com/datasets/shayanfazeli/heartbeat
+
+### What I have done
+Firstly I have visulized the data using different data visualization techniques and after that the model creation is started.
+
+### Libraries used
+1. `Numpy`
+2. `Pandas`
+3. `sklearn`
+4. `Keras`
+5. `Tensorflow`
+6. `matplotlib`
+
+
+# Data Visualization And Conclusion #
+
+### Content of the Training Data set ###
+
+![image](https://user-images.githubusercontent.com/83596240/183436392-f412bd71-0330-4bc5-bcde-41f78583a959.png)
+
+### Different classes/categories along with their frequency ###
+
+![image](https://user-images.githubusercontent.com/83596240/183436723-a8930a81-472e-4cf6-81ee-136cc47db7c8.png)
+
+### Depicting each class in percentage wise using a pie plot ###
+
+![image](https://user-images.githubusercontent.com/83596240/183436966-228c24f1-6e79-4a64-ba47-8aadba9c8c24.png)
+
+We can see that Normal beats as the highest percentage and Fusion beats as the lowest
+
+### Normalizing and Resizing the Training Data set ###
+
+![image](https://user-images.githubusercontent.com/83596240/183437696-c1d54039-6043-4e74-a344-92d872eedadc.png)
+
+The resultant Training Data set that we get is
+
+![image](https://user-images.githubusercontent.com/83596240/183437918-93bcb404-d275-4cff-8deb-12cd9eacb38c.png)
+
+### Updated classes/categories along with their frequency ###
+
+![image](https://user-images.githubusercontent.com/83596240/183438062-ac62948e-fd33-4662-b8f3-4860bdfa9576.png)
+
+As we can see the training dataset is equally balanced with each category having frequency of 16000
+
+### Depicting each class in percentage wise using a pie plot ###
+
+![image](https://user-images.githubusercontent.com/83596240/183438173-e9899fa1-f644-4f1d-852a-2190d20e50d8.png)
+
+### Sample data from each categories ###
+
+![image](https://user-images.githubusercontent.com/83596240/183438386-e32a0d44-788f-4ce1-819d-5666c39df86f.png)
+
+### Checking the Testing Accuracy and Loss ###
+
+![image](https://user-images.githubusercontent.com/83596240/183439120-d751e678-a494-4ac5-ad38-89bd42c65665.png)
+
+The accuracy of the trained model stands at 96.67% and loss is 13.4%
+
+Graphing the training accuracy and loss against the testing acurracy and loss
+
+![image](https://user-images.githubusercontent.com/83596240/183438969-d446f376-582a-4283-a67d-2e7f71ececc6.png)
+
+As the epochs increases the loss decreses and the accuracy increases
+
+### Confusion matrics of the trained model ###
+
+![image](https://user-images.githubusercontent.com/83596240/183439764-f22613f0-68d8-4153-a5ed-85a3db911727.png)
+
+### Training the model with noise to avoid overfitting ###
+
+![image](https://user-images.githubusercontent.com/83596240/183439990-49f9aa6b-b425-432d-b371-9e192fd4a7fa.png)
+
+### Checking the Testing Accuracy and Loss of the new model ###
+
+![image](https://user-images.githubusercontent.com/83596240/183440057-23200f9e-c8c8-49a2-b397-1409695a2d74.png)
+
+The accuracy as reduced to 76.3% and the loss as increased to 132% when we add noise to the dataset
+
+Confusion matrics of the newly trained model
+
+![image](https://user-images.githubusercontent.com/83596240/183440297-ebee69b5-82bd-4ff8-80fe-302b955d9f1a.png)
+
+### This the Confusion metrics that we get after applying transfer learning and performing fine tune to the noisy model ###
+
+![image](https://user-images.githubusercontent.com/83596240/183440719-34265311-faea-46c7-9759-dc38fdf1f345.png)
+
diff --git a/Heartbeat Classification using ECG/Model/ecg-cnn-model01.ipynb b/Heartbeat Classification using ECG/Model/ecg-cnn-model01.ipynb
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index 00000000..bf451de6
--- /dev/null
+++ b/Heartbeat Classification using ECG/Model/ecg-cnn-model01.ipynb	
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this model we will use two famous datasets for heartbeat classification:\n",
+    "* MIT-BIH Arrhythmia Dataset\n",
+    "\n",
+    "The datasets contain signals that correspond to the shapes of heartbeats '\\n'\n",
+    "obtained by an electrocardiogram (ECG). These signals are preprocessed \\n\n",
+    "and segmented so that each segment corresponds to a beat.\n",
+    "\n",
+    "**MIT-BIH Arrythmia Dataset**\n",
+    "\n",
+    "    Number of Samples: 109446\n",
+    "    Number of Categories: 5\n",
+    "    Sampling Frequency: 125Hz\n",
+    "    Data Source: Physionet's MIT-BIH Arrhythmia Dataset\n",
+    "    Classes: ['N': 0, 'S': 1, 'V': 2, 'F': 3, 'Q': 4]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ECG-CLASSIFICATION OF SIGNALS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:32:14.434364Z",
+     "iopub.status.busy": "2022-08-06T04:32:14.433583Z",
+     "iopub.status.idle": "2022-08-06T04:32:21.353541Z",
+     "shell.execute_reply": "2022-08-06T04:32:21.352443Z",
+     "shell.execute_reply.started": "2022-08-06T04:32:14.434318Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from keras.utils.np_utils import to_categorical\n",
+    "from sklearn.utils import class_weight,resample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:32:21.357674Z",
+     "iopub.status.busy": "2022-08-06T04:32:21.356039Z",
+     "iopub.status.idle": "2022-08-06T04:32:32.124017Z",
+     "shell.execute_reply": "2022-08-06T04:32:32.122823Z",
+     "shell.execute_reply.started": "2022-08-06T04:32:21.357622Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "train_df = pd.read_csv('datasets_ecg/mitbih_train.csv',header=None)\n",
+    "tests_df = pd.read_csv('datasets_ecg/mitbih_test.csv',header=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:32:32.125836Z",
+     "iopub.status.busy": "2022-08-06T04:32:32.125467Z",
+     "iopub.status.idle": "2022-08-06T04:32:32.185169Z",
+     "shell.execute_reply": "2022-08-06T04:32:32.183851Z",
+     "shell.execute_reply.started": "2022-08-06T04:32:32.125802Z"
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87552</th>\n",
+       "      <td>0.858228</td>\n",
+       "      <td>0.645570</td>\n",
+       "      <td>0.845570</td>\n",
+       "      <td>0.248101</td>\n",
+       "      <td>0.167089</td>\n",
+       "      <td>0.131646</td>\n",
+       "      <td>0.121519</td>\n",
+       "      <td>0.121519</td>\n",
+       "      <td>0.118987</td>\n",
+       "      <td>0.103797</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87553</th>\n",
+       "      <td>0.901506</td>\n",
+       "      <td>0.845886</td>\n",
+       "      <td>0.800695</td>\n",
+       "      <td>0.748552</td>\n",
+       "      <td>0.687138</td>\n",
+       "      <td>0.599073</td>\n",
+       "      <td>0.512167</td>\n",
+       "      <td>0.427578</td>\n",
+       "      <td>0.395133</td>\n",
+       "      <td>0.402086</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>87554 rows × 188 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            0         1         2         3         4         5         6    \\\n",
+       "0      0.977941  0.926471  0.681373  0.245098  0.154412  0.191176  0.151961   \n",
+       "1      0.960114  0.863248  0.461538  0.196581  0.094017  0.125356  0.099715   \n",
+       "2      1.000000  0.659459  0.186486  0.070270  0.070270  0.059459  0.056757   \n",
+       "3      0.925414  0.665746  0.541436  0.276243  0.196133  0.077348  0.071823   \n",
+       "4      0.967136  1.000000  0.830986  0.586854  0.356808  0.248826  0.145540   \n",
+       "...         ...       ...       ...       ...       ...       ...       ...   \n",
+       "87549  0.807018  0.494737  0.536842  0.529825  0.491228  0.484211  0.456140   \n",
+       "87550  0.718333  0.605000  0.486667  0.361667  0.231667  0.120000  0.051667   \n",
+       "87551  0.906122  0.624490  0.595918  0.575510  0.530612  0.481633  0.444898   \n",
+       "87552  0.858228  0.645570  0.845570  0.248101  0.167089  0.131646  0.121519   \n",
+       "87553  0.901506  0.845886  0.800695  0.748552  0.687138  0.599073  0.512167   \n",
+       "\n",
+       "            7         8         9    ...  178  179  180  181  182  183  184  \\\n",
+       "0      0.085784  0.058824  0.049020  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "1      0.088319  0.074074  0.082621  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "2      0.043243  0.054054  0.045946  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "3      0.060773  0.066298  0.058011  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "4      0.089202  0.117371  0.150235  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "...         ...       ...       ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
+       "87549  0.396491  0.284211  0.136842  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "87550  0.001667  0.000000  0.013333  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "87551  0.387755  0.322449  0.191837  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "87552  0.121519  0.118987  0.103797  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "87553  0.427578  0.395133  0.402086  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "\n",
+       "       185  186  187  \n",
+       "0      0.0  0.0  0.0  \n",
+       "1      0.0  0.0  0.0  \n",
+       "2      0.0  0.0  0.0  \n",
+       "3      0.0  0.0  0.0  \n",
+       "4      0.0  0.0  0.0  \n",
+       "...    ...  ...  ...  \n",
+       "87549  0.0  0.0  4.0  \n",
+       "87550  0.0  0.0  4.0  \n",
+       "87551  0.0  0.0  4.0  \n",
+       "87552  0.0  0.0  4.0  \n",
+       "87553  0.0  0.0  4.0  \n",
+       "\n",
+       "[87554 rows x 188 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:32:32.188076Z",
+     "iopub.status.busy": "2022-08-06T04:32:32.187712Z",
+     "iopub.status.idle": "2022-08-06T04:32:32.203289Z",
+     "shell.execute_reply": "2022-08-06T04:32:32.202010Z",
+     "shell.execute_reply.started": "2022-08-06T04:32:32.188041Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0    72471\n",
+      "4.0     6431\n",
+      "2.0     5788\n",
+      "1.0     2223\n",
+      "3.0      641\n",
+      "Name: 187, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "classes = train_df[187].value_counts()\n",
+    "print(classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:32:32.205068Z",
+     "iopub.status.busy": "2022-08-06T04:32:32.204726Z",
+     "iopub.status.idle": "2022-08-06T04:32:32.403083Z",
+     "shell.execute_reply": "2022-08-06T04:32:32.401852Z",
+     "shell.execute_reply.started": "2022-08-06T04:32:32.205037Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7,7))\n",
+    "explode = (0,0.1,0.1,0.1,0.1)\n",
+    "plt.pie(classes,labels=['Normal beats','Unknown beats','Ventricular ectopic beats','Supraventricular ectopic beats','Fusion beats'], autopct='%1.1f%%', explode=explode)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:32:32.407124Z",
+     "iopub.status.busy": "2022-08-06T04:32:32.406533Z",
+     "iopub.status.idle": "2022-08-06T04:32:32.818731Z",
+     "shell.execute_reply": "2022-08-06T04:32:32.817643Z",
+     "shell.execute_reply.started": "2022-08-06T04:32:32.407078Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_0 = train_df[train_df[187]==0.0].sample(n=16000, random_state=1)\n",
+    "df_1 = train_df[train_df[187]==1.0]\n",
+    "df_2 = train_df[train_df[187]==2.0]\n",
+    "df_3 = train_df[train_df[187]==3.0]\n",
+    "df_4 = train_df[train_df[187]==4.0]\n",
+    "# upsample\n",
+    "df_1_up=resample(df_1, replace=True, n_samples=16000, random_state=1)\n",
+    "df_2_up=resample(df_2, replace=True, n_samples=16000, random_state=1)\n",
+    "df_3_up=resample(df_3, replace=True, n_samples=16000, random_state=1)\n",
+    "df_4_up=resample(df_4, replace=True, n_samples=16000, random_state=1)\n",
+    "\n",
+    "train_df = pd.concat([df_0, df_1_up, df_2_up, df_3_up, df_4_up])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:32:32.820423Z",
+     "iopub.status.busy": "2022-08-06T04:32:32.820063Z",
+     "iopub.status.idle": "2022-08-06T04:32:32.862706Z",
+     "shell.execute_reply": "2022-08-06T04:32:32.861676Z",
+     "shell.execute_reply.started": "2022-08-06T04:32:32.820347Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+       "      <td>0.322086</td>\n",
+       "      <td>0.346626</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49665</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.059006</td>\n",
+       "      <td>0.236025</td>\n",
+       "      <td>0.347826</td>\n",
+       "      <td>0.434783</td>\n",
+       "      <td>0.431677</td>\n",
+       "      <td>0.422360</td>\n",
+       "      <td>0.437888</td>\n",
+       "      <td>0.456522</td>\n",
+       "      <td>0.453416</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30911</th>\n",
+       "      <td>0.975659</td>\n",
+       "      <td>0.872211</td>\n",
+       "      <td>0.531440</td>\n",
+       "      <td>0.255578</td>\n",
+       "      <td>0.184584</td>\n",
+       "      <td>0.162272</td>\n",
+       "      <td>0.109533</td>\n",
+       "      <td>0.113590</td>\n",
+       "      <td>0.113590</td>\n",
+       "      <td>0.107505</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85165</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.567164</td>\n",
+       "      <td>0.570895</td>\n",
+       "      <td>0.585821</td>\n",
+       "      <td>0.585821</td>\n",
+       "      <td>0.563433</td>\n",
+       "      <td>0.574627</td>\n",
+       "      <td>0.522388</td>\n",
+       "      <td>0.402985</td>\n",
+       "      <td>0.298507</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86756</th>\n",
+       "      <td>0.664697</td>\n",
+       "      <td>0.589365</td>\n",
+       "      <td>0.483013</td>\n",
+       "      <td>0.392910</td>\n",
+       "      <td>0.273264</td>\n",
+       "      <td>0.162482</td>\n",
+       "      <td>0.087149</td>\n",
+       "      <td>0.031019</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.026588</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83845</th>\n",
+       "      <td>0.945148</td>\n",
+       "      <td>0.518987</td>\n",
+       "      <td>0.556962</td>\n",
+       "      <td>0.556962</td>\n",
+       "      <td>0.544304</td>\n",
+       "      <td>0.510549</td>\n",
+       "      <td>0.527426</td>\n",
+       "      <td>0.459916</td>\n",
+       "      <td>0.337553</td>\n",
+       "      <td>0.206751</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83949</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.505618</td>\n",
+       "      <td>0.535581</td>\n",
+       "      <td>0.520599</td>\n",
+       "      <td>0.528090</td>\n",
+       "      <td>0.524345</td>\n",
+       "      <td>0.531835</td>\n",
+       "      <td>0.479401</td>\n",
+       "      <td>0.404494</td>\n",
+       "      <td>0.303371</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85673</th>\n",
+       "      <td>0.722689</td>\n",
+       "      <td>0.509804</td>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.462185</td>\n",
+       "      <td>0.445378</td>\n",
+       "      <td>0.431373</td>\n",
+       "      <td>0.411765</td>\n",
+       "      <td>0.386555</td>\n",
+       "      <td>0.341737</td>\n",
+       "      <td>0.271709</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>80000 rows × 188 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            0         1         2         3         4         5         6    \\\n",
+       "65728  1.000000  0.918864  0.567951  0.243408  0.279919  0.310345  0.283976   \n",
+       "33224  0.199288  0.074733  0.270463  0.427046  0.551601  0.569395  0.558719   \n",
+       "46733  1.000000  0.929448  0.328221  0.070552  0.205521  0.294479  0.309816   \n",
+       "49665  0.000000  0.059006  0.236025  0.347826  0.434783  0.431677  0.422360   \n",
+       "30911  0.975659  0.872211  0.531440  0.255578  0.184584  0.162272  0.109533   \n",
+       "...         ...       ...       ...       ...       ...       ...       ...   \n",
+       "85165  1.000000  0.567164  0.570895  0.585821  0.585821  0.563433  0.574627   \n",
+       "86756  0.664697  0.589365  0.483013  0.392910  0.273264  0.162482  0.087149   \n",
+       "83845  0.945148  0.518987  0.556962  0.556962  0.544304  0.510549  0.527426   \n",
+       "83949  1.000000  0.505618  0.535581  0.520599  0.528090  0.524345  0.531835   \n",
+       "85673  0.722689  0.509804  0.470588  0.462185  0.445378  0.431373  0.411765   \n",
+       "\n",
+       "            7         8         9    ...  178  179  180  181  182  183  184  \\\n",
+       "65728  0.283976  0.277890  0.267748  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "33224  0.565836  0.594306  0.597865  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "46733  0.319018  0.322086  0.346626  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "49665  0.437888  0.456522  0.453416  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "30911  0.113590  0.113590  0.107505  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "...         ...       ...       ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
+       "85165  0.522388  0.402985  0.298507  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "86756  0.031019  0.000000  0.026588  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "83845  0.459916  0.337553  0.206751  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "83949  0.479401  0.404494  0.303371  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "85673  0.386555  0.341737  0.271709  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
+       "\n",
+       "       185  186  187  \n",
+       "65728  0.0  0.0  0.0  \n",
+       "33224  0.0  0.0  0.0  \n",
+       "46733  0.0  0.0  0.0  \n",
+       "49665  0.0  0.0  0.0  \n",
+       "30911  0.0  0.0  0.0  \n",
+       "...    ...  ...  ...  \n",
+       "85165  0.0  0.0  4.0  \n",
+       "86756  0.0  0.0  4.0  \n",
+       "83845  0.0  0.0  4.0  \n",
+       "83949  0.0  0.0  4.0  \n",
+       "85673  0.0  0.0  4.0  \n",
+       "\n",
+       "[80000 rows x 188 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:32:32.864611Z",
+     "iopub.status.busy": "2022-08-06T04:32:32.864260Z",
+     "iopub.status.idle": "2022-08-06T04:32:32.873202Z",
+     "shell.execute_reply": "2022-08-06T04:32:32.871521Z",
+     "shell.execute_reply.started": "2022-08-06T04:32:32.864580Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0    16000\n",
+      "1.0    16000\n",
+      "2.0    16000\n",
+      "3.0    16000\n",
+      "4.0    16000\n",
+      "Name: 187, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "classes = train_df[187].value_counts()\n",
+    "print(classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:32:32.875082Z",
+     "iopub.status.busy": "2022-08-06T04:32:32.874673Z",
+     "iopub.status.idle": "2022-08-06T04:32:32.999764Z",
+     "shell.execute_reply": "2022-08-06T04:32:32.998460Z",
+     "shell.execute_reply.started": "2022-08-06T04:32:32.875051Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7,7))\n",
+    "plt.pie(classes,labels=['Normal beats','Unknown beats','Ventricular ectopic beats','Supraventricular ectopic beats','Fusion beats'], autopct='%1.1f%%')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### We will analyze some samples of the classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:32:33.004618Z",
+     "iopub.status.busy": "2022-08-06T04:32:33.003501Z",
+     "iopub.status.idle": "2022-08-06T04:32:33.525567Z",
+     "shell.execute_reply": "2022-08-06T04:32:33.524390Z",
+     "shell.execute_reply.started": "2022-08-06T04:32:33.004568Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Magnitude')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x648 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sample_n=df_0.sample(1)\n",
+    "sample_s=df_1.sample(1)\n",
+    "sample_v=df_2.sample(1)\n",
+    "sample_f=df_3.sample(1)\n",
+    "sample_q=df_4.sample(1)\n",
+    "\n",
+    "fig,ax = plt.subplots(5,sharex=True,sharey=True)\n",
+    "fig.set_size_inches(9,9)\n",
+    "\n",
+    "ax[0].plot(sample_n.iloc[0,:186], color='red')\n",
+    "ax[0].set_title(\"Normal beats\")\n",
+    "ax[1].plot(sample_s.iloc[0,:186], color='red')\n",
+    "ax[1].set_title(\"Supraventricular ectopic beats\")\n",
+    "ax[2].plot(sample_v.iloc[0,:186], color='red')\n",
+    "ax[2].set_title(\"Ventricular ectopic beats\")\n",
+    "ax[3].plot(sample_f.iloc[0,:186], color='red')\n",
+    "ax[3].set_title(\"Fusion beats\")\n",
+    "ax[4].plot(sample_q.iloc[0,:186], color='red')\n",
+    "ax[4].set_title(\"Unknown beats\")\n",
+    "plt.xlabel('Time')\n",
+    "plt.ylabel('Magnitude')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:32:33.527823Z",
+     "iopub.status.busy": "2022-08-06T04:32:33.527346Z",
+     "iopub.status.idle": "2022-08-06T04:32:33.538558Z",
+     "shell.execute_reply": "2022-08-06T04:32:33.537423Z",
+     "shell.execute_reply.started": "2022-08-06T04:32:33.527779Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "Y_train = to_categorical(train_df[187])\n",
+    "Y_tests = to_categorical(tests_df[187])\n",
+    "\n",
+    "X_train = train_df.iloc[:,:186].values\n",
+    "X_train = X_train.reshape(len(X_train), X_train.shape[1],1)\n",
+    "X_tests = tests_df.iloc[:,:186].values\n",
+    "X_tests = X_tests.reshape(len(X_tests), X_tests.shape[1],1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Convolutional neural network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:32:33.540977Z",
+     "iopub.status.busy": "2022-08-06T04:32:33.540213Z",
+     "iopub.status.idle": "2022-08-06T04:32:33.800446Z",
+     "shell.execute_reply": "2022-08-06T04:32:33.798946Z",
+     "shell.execute_reply.started": "2022-08-06T04:32:33.540930Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers import Conv1D, MaxPooling1D, Flatten\n",
+    "from keras.layers import Dense, Dropout, BatchNormalization\n",
+    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
+    "from keras.callbacks import History \n",
+    "\n",
+    "history = History()\n",
+    "Model = Sequential()\n",
+    "Model.add(Conv1D(128,3,\n",
+    "                 input_shape=(X_train.shape[1],1),\n",
+    "                 activation='relu'))\n",
+    "\n",
+    "Model.add(BatchNormalization())\n",
+    "Model.add(MaxPooling1D(pool_size=2))\n",
+    "\n",
+    "Model.add(Conv1D(64,3, activation='relu'))\n",
+    "Model.add(BatchNormalization())\n",
+    "Model.add(MaxPooling1D(pool_size=2))\n",
+    "\n",
+    "Model.add(Conv1D(64,2, activation='relu'))\n",
+    "Model.add(BatchNormalization())\n",
+    "Model.add(MaxPooling1D(pool_size=2))\n",
+    "\n",
+    "Model.add(Conv1D(64,2, activation='relu'))\n",
+    "\n",
+    "Model.add(Flatten())\n",
+    "\n",
+    "Model.add(Dense(128, activation='relu'))\n",
+    "\n",
+    "Model.add(Dropout(0.5))\n",
+    "\n",
+    "Model.add(Dense(64,  activation='relu'))\n",
+    "\n",
+    "Model.add(Dense(5,activation='softmax'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:32:33.806411Z",
+     "iopub.status.busy": "2022-08-06T04:32:33.803462Z",
+     "iopub.status.idle": "2022-08-06T04:32:33.826291Z",
+     "shell.execute_reply": "2022-08-06T04:32:33.825243Z",
+     "shell.execute_reply.started": "2022-08-06T04:32:33.806356Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "Model.compile(\n",
+    "    loss='categorical_crossentropy',\n",
+    "    optimizer='adam',\n",
+    "    metrics=['accuracy']\n",
+    ")\n",
+    "\n",
+    "callbacks = [\n",
+    "    EarlyStopping(monitor='val_loss', patience=8), \n",
+    "    ModelCheckpoint(filepath='./best_weights.h5', monitor='val_loss', save_best_only=True)\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:32:33.828161Z",
+     "iopub.status.busy": "2022-08-06T04:32:33.827808Z",
+     "iopub.status.idle": "2022-08-06T04:32:33.839778Z",
+     "shell.execute_reply": "2022-08-06T04:32:33.838715Z",
+     "shell.execute_reply.started": "2022-08-06T04:32:33.828129Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " conv1d (Conv1D)             (None, 184, 128)          512       \n",
+      "                                                                 \n",
+      " batch_normalization (BatchN  (None, 184, 128)         512       \n",
+      " ormalization)                                                   \n",
+      "                                                                 \n",
+      " max_pooling1d (MaxPooling1D  (None, 92, 128)          0         \n",
+      " )                                                               \n",
+      "                                                                 \n",
+      " conv1d_1 (Conv1D)           (None, 90, 64)            24640     \n",
+      "                                                                 \n",
+      " batch_normalization_1 (Batc  (None, 90, 64)           256       \n",
+      " hNormalization)                                                 \n",
+      "                                                                 \n",
+      " max_pooling1d_1 (MaxPooling  (None, 45, 64)           0         \n",
+      " 1D)                                                             \n",
+      "                                                                 \n",
+      " conv1d_2 (Conv1D)           (None, 44, 64)            8256      \n",
+      "                                                                 \n",
+      " batch_normalization_2 (Batc  (None, 44, 64)           256       \n",
+      " hNormalization)                                                 \n",
+      "                                                                 \n",
+      " max_pooling1d_2 (MaxPooling  (None, 22, 64)           0         \n",
+      " 1D)                                                             \n",
+      "                                                                 \n",
+      " conv1d_3 (Conv1D)           (None, 21, 64)            8256      \n",
+      "                                                                 \n",
+      " flatten (Flatten)           (None, 1344)              0         \n",
+      "                                                                 \n",
+      " dense (Dense)               (None, 128)               172160    \n",
+      "                                                                 \n",
+      " dropout (Dropout)           (None, 128)               0         \n",
+      "                                                                 \n",
+      " dense_1 (Dense)             (None, 64)                8256      \n",
+      "                                                                 \n",
+      " dense_2 (Dense)             (None, 5)                 325       \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 223,429\n",
+      "Trainable params: 222,917\n",
+      "Non-trainable params: 512\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "Model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:32:33.842244Z",
+     "iopub.status.busy": "2022-08-06T04:32:33.841140Z",
+     "iopub.status.idle": "2022-08-06T04:45:57.647796Z",
+     "shell.execute_reply": "2022-08-06T04:45:57.646379Z",
+     "shell.execute_reply.started": "2022-08-06T04:32:33.842206Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/12\n",
+      "2500/2500 [==============================] - 93s 37ms/step - loss: 0.3396 - accuracy: 0.8755 - val_loss: 0.3401 - val_accuracy: 0.8919\n",
+      "Epoch 2/12\n",
+      "2500/2500 [==============================] - 97s 39ms/step - loss: 0.1640 - accuracy: 0.9413 - val_loss: 0.2383 - val_accuracy: 0.9159\n",
+      "Epoch 3/12\n",
+      "2500/2500 [==============================] - 96s 39ms/step - loss: 0.1154 - accuracy: 0.9588 - val_loss: 0.1704 - val_accuracy: 0.9416\n",
+      "Epoch 4/12\n",
+      "2500/2500 [==============================] - 95s 38ms/step - loss: 0.0922 - accuracy: 0.9669 - val_loss: 0.1604 - val_accuracy: 0.9468\n",
+      "Epoch 5/12\n",
+      "2500/2500 [==============================] - 91s 36ms/step - loss: 0.0754 - accuracy: 0.9740 - val_loss: 0.1387 - val_accuracy: 0.9572\n",
+      "Epoch 6/12\n",
+      "2500/2500 [==============================] - 92s 37ms/step - loss: 0.0653 - accuracy: 0.9773 - val_loss: 0.1288 - val_accuracy: 0.9619\n",
+      "Epoch 7/12\n",
+      "2500/2500 [==============================] - 93s 37ms/step - loss: 0.0581 - accuracy: 0.9804 - val_loss: 0.1554 - val_accuracy: 0.9532\n",
+      "Epoch 8/12\n",
+      "2500/2500 [==============================] - 91s 36ms/step - loss: 0.0514 - accuracy: 0.9824 - val_loss: 0.1148 - val_accuracy: 0.9677\n",
+      "Epoch 9/12\n",
+      "2500/2500 [==============================] - 87s 35ms/step - loss: 0.0470 - accuracy: 0.9844 - val_loss: 0.1441 - val_accuracy: 0.9598\n",
+      "Epoch 10/12\n",
+      "2500/2500 [==============================] - 95s 38ms/step - loss: 0.0423 - accuracy: 0.9859 - val_loss: 0.1274 - val_accuracy: 0.9656\n",
+      "Epoch 11/12\n",
+      "2500/2500 [==============================] - 93s 37ms/step - loss: 0.0404 - accuracy: 0.9868 - val_loss: 0.1202 - val_accuracy: 0.9667\n",
+      "Epoch 12/12\n",
+      "2500/2500 [==============================] - 92s 37ms/step - loss: 0.0374 - accuracy: 0.9875 - val_loss: 0.1169 - val_accuracy: 0.9723\n"
+     ]
+    }
+   ],
+   "source": [
+    "eps = 12\n",
+    "bts = 32\n",
+    "\n",
+    "history = Model.fit(X_train, Y_train, \n",
+    "          epochs=eps, callbacks=callbacks,\n",
+    "          batch_size=bts, validation_data=(X_tests,Y_tests))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:45:57.650742Z",
+     "iopub.status.busy": "2022-08-06T04:45:57.650234Z",
+     "iopub.status.idle": "2022-08-06T04:45:58.055035Z",
+     "shell.execute_reply": "2022-08-06T04:45:58.053518Z",
+     "shell.execute_reply.started": "2022-08-06T04:45:57.650693Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+7kudfxZkjoSiR3o0JmOMCSVLEAEanJbAsIxE/+MQUVEw5UYoKYLS5T0emzHGhIIliC4ozM+gaPMe//eWmHglRCfYlFdjTMSwBNEFhfkZ7K1vYX157cE7E9LhxMtcfaaGvQfvN8aYPsYSRBcU5nWMQxyiQF/BzdDaYPWZjDERwRJEFwzPTCQ7Jc7/OATA4BMhpxCK/mz1mYwxfZ4liC4QEQrzM1iy6RDjEODqM+3ZABvf7tngjDEmyCxBdNHJ+RnsqG6kZG+D/wPGzoTEgTZYbYzp8yxBdFFB/hHGIaLj4KRrXH2myq09GJkxxgSXJYguGp2dQlpCDEv8LZjrMOV697PY6jMZY/ouSxBdFBUlFORlULT5MFNZ04fB6OmuPlNrU88FZ4wxQWQJohtOzs9g0646yqsPU5yv4Cao32X1mYwxfZYliG7oqMu0ZPMhxiEAjp0KGcfaYLUxps+yBNEN44akkhjrOfR6CHD1mQpugm0fQtnHPRecMcYEiSWIboj2RDF5+IDDJwiw+kzGmD4tpAlCRKaLyGcisl5E7vKz/6si8rH3sUhEJvjs2ywiK0VkuYgUhzLO7ijMy+CznTVU1jcf+qCEATD+Ulj5d2io7LHYjDEmGEKWIETEA9wPzADGAleIyNhOh20CzlTVE4GfAXM67Z+qqhNVdUqo4uyuwvwMVKH4cLOZwHUztdTDiqd7JjBjjAmSULYgCoH1qrpRVZuBZ4CZvgeo6iJV7fgNuxjICWE8QTUhN51YT9ThB6oBhkyEnALXzWT1mYwxfUgoE8RQYJvP6xLvtkO5Efinz2sFForIUhGZfaiTRGS2iBSLSHFFRcVRBdwV8TEeJuSmHXpFta+Cm2H3etj0bugDM8aYIAllghA/2/xWuBORqbgE8X2fzaer6km4LqpviMgZ/s5V1TmqOkVVp2RlZR1tzF1SmJ/BJ9urqGtqPfyBY2dCYqYNVhtj+pRQJogSINfndQ5Q2vkgETkReBSYqar76leoaqn3ZznwEq7LqlcpzM+ktV35aGvl4Q+MiXf1mT6bD5XbDn+sMcb0EqFMEEXAKBHJF5FY4HJgnu8BIjIMeBG4WlXX+mxPEpGUjufANGBVCGPtlsnDBxAlHL4u076DrwdVWPrXkMdljDHBELIEoaqtwK3AAmA18JyqfiIit4jILd7DfgxkAg90ms46CHhfRFYAS4DXVPX1UMXaXclx0YwbEuA4xIDh3vpMj1l9JmNMnxAdyour6nxgfqdtD/k8vwm4yc95G4EJnbf3RoX5Gfxt8RaaWtuIi/Yc/uCCm2DtP2H1K259hDHG9GK2kvooFeZn0NzazsqSqiMfPOJsGJAPSx4JfWDGGHOULEEcpYK8I9xAyFdUFBTcCNsWw7YlIY7MGGOOjiWIo5SRFMvoQclHrsvUYfJ1kDIEXv0OtLWENDZjjDkaliCCoCAvg6Vb9tLW7neZx4HiUuC8X8HOVbD4wdAHZ4wx3WQJIggK8zOobWpldVl1YCccfwEcdx688wu7b7UxpteyBBEEHTcQCmgcAkAEZvwKEHjtDrc+whhjehlLEEEwOC2BYRmJgS2Y65CeC1P/G9YtgNXzjny8Mcb0MEsQQVKQl0HR5r1oV1oDJ98Cx4yH+d+DxgCmyRpjTA+yBBEkJ+dnsKeumQ0VtYGf5ImGC++D2p3w1v8LXXDGGNMNliCCpMvjEB2GTobCm93iue1LQxCZMcZ0jyWIIBmemUh2Slzg6yF8nf1DSDkGXvk2tB2hdLgxxvQQSxBBIiIU5mfw4cY9XRuHAIhPgxm/hB0r4cOHjny8Mcb0AEsQQVSYn8GO6kZK9jZ0/eQxX3LVXt/+ud0zwhjTK1iCCKKOcYhudTOJwHm/ds/n32lrI4wxYWcJIohGZ6eQlhDTvQQBkD4MzrrblQRf82pwgzPGmC6yBBFEUVFCQV4GSzZ3M0EAnPJ1GNSxNiLA0h3GGBMCliCC7OT8DDbtqqO8prF7F/DEwIX3Qk2ZG48wxpgwsQQRZAXecYiiTXu7f5GcKe6+EUvmwPZlQYrMGGO6xhJEkI0bkkpirKdrdZn8OefHkJQFr95mayOMMWFhCSLIYjxRTB4+oOsrqjvrWBtRtsK1JIwxpodZggiBwrwMPttZQ2V989FdaOxFMGqaq9NUVRKU2IwxJlCWIEKgID8DVSjefBTjEOBdG/Eb0Hb45/eDE5wxxgTIEkQITMxNJ9YTRdHRTHftMGA4nHWXWxex2tZGGGN6jiWIEIiP8TAhN+3oxyE6nPoNyB4H//weNNUE55rGGHMEIU0QIjJdRD4TkfUicpef/V8VkY+9j0UiMiHQc3u7wvwMVm2voq4pCDOQOtZGVJfC2/939NczxpgAhCxBiIgHuB+YAYwFrhCRsZ0O2wScqaonAj8D5nTh3F6tMD+T1nblo62VwblgbiFMud5Vey1dHpxrGmPMYYSyBVEIrFfVjaraDDwDzPQ9QFUXqWrHSO5iICfQc3u7k4alEyUcXdmNzs75CSQOdPeNaG8L3nWNMcaPUCaIoYBv3eoS77ZDuRH4Z1fPFZHZIlIsIsUVFRVHEW5wpcTHMG5I2tEvmPOVkA4z7oGy5e4OdMYYE0IBJQgR+baIpIrzZxFZJiLTjnSan21+a1iLyFRcguiYyxnwuao6R1WnqOqUrKysI4TUswrzM/hoayVNrUH8tj/uyzDyC/DWz6Bqe/Cua4wxnQTagrhBVauBaUAWcD1wzxHOKQFyfV7nAKWdDxKRE4FHgZmqursr5/Z2hfkZNLW2s7KkKngX7Vgb0d7qZjUZY0yIBJogOr7Rnwf8RVVX4P9bvq8iYJSI5ItILHA5MO+Ai4oMA14ErlbVtV05ty8oyHOF+4I23bVDRj6c+X23NmLN/OBe2xhjvAJNEEtFZCEuQSwQkRSg/XAnqGorcCuwAFgNPKeqn4jILSJyi/ewHwOZwAMislxEig93bhc/W9hlJMUyKjs5OAvmOjvtm5A91t19rqk2+Nc3xvR7ogHc2lJEooCJwEZVrRSRDCBHVT8OcXxdMmXKFC0uLg53GAf4wUsreXl5KSt+Mg1P1JEaXV209UOYOw1OvRW+aPeOMMZ0nYgsVdUp/vYF2oI4FfjMmxyuAn4IBLFjPXIV5mdQ29TK6rIQ3B1u2Mkw+TpY/ICr+mqMMUEUaIJ4EKj3rnT+HrAFeDxkUUWQwvwQjUN0+ML/QGImvHKbrY0wxgRVoAmiVV1f1EzgPlW9D0gJXViRY3BaArkZCRSFKkEkDIDp90DpMij6c2jewxjTLwWaIGpE5G7gauA1bymMmNCFFVkK8zJZsnkPgYz3dMsJl8CIs+HN/3X1mowxJggCTRBfAZpw6yF24FY1/zpkUUWYk/Mz2FPXzIaKEM02EoHzfwvtLXbfCGNM0ASUILxJ4UkgTUQuABpV1cYgAhTycQiAjGPhjDth9Tz47PXQvY8xpt8ItNTGLGAJcBkwC/hQRC4NZWCRZHhmItkpcaEbh+hw2rcg63iYfwc014X2vYwxES/QLqYfAAWqeq2qXoOrtvqj0IUVWUSEgvwMPtwUwnEIgOhYuOBeqNoG7/widO9jjOkXAk0QUapa7vN6dxfONbhxiLKqRkr2NoT2jYafCiddAx88ADtWhva9jDERLdBf8q+LyAIRuU5ErgNeA6wIUBd0jEMsCXU3E8AXfuqmv9raCGPMUQh0kPpO3N3eTgQmAHNU1abLdMHo7BTSEmJ6JkEkZsD0X8D2YiieG/r3M8ZEpOhAD1TVF4AXQhhLRIuKEgryBoSmcJ8/4y+D5U+6tRHHXwCpg3vmfY0xEeOwLQgRqRGRaj+PGhEJQXGhyFaYn8HGXXWU1zSG/s1E4PzfQWsTPHcNLHscdq2HUA6SG2MiymFbEKpq5TSCqDA/E4CiTXs5/8Qe+EafOQLO+xW88VOY9023LSkLhp0Cw05zA9qDxoMn4IakMaYfsd8MPWjckFQSYz0s2bS7ZxIEuGqvk66B3etgyyLYuhi2LoLVr7j9scmQUwDDT3OJY+gUiE3smdiMMb2aJYgeFOOJYvLwASzZvLdn3zgqCrKOc48p17ttVdth6wfex2J4+/8AhahoGDzRtS6GeZNGYkbPxmuM6RUsQfSwgrwMfv/GWirrm0lPjA1fIGlDYfyl7gHQsBe2LXEJY8sH8OHDsOiPbl/W8Qd2S6XlujEOY0xEC+iOcn1Fb7yjXGertldx4Z/e59wxg3jwqsnBv8tcsLQ0uhLiHQlj24fQ5J2XkDoUhp3qbWWcClljXCvFGNPnHO6OcpYgwuAv/9nET1/5lOtPz+MnF44LdziBaW+D8k9dsujomqopc/vi0yD3lP3dUkMn28C3MX3E4RKE/S8Og+tPz2fbngbm/mcTuQMSueFz+eEO6ciiPHDMePc4ebabLrt38/5B762LYd0Cd2zGsXDmXa77KsoT1rCNMd1nCSJMfnD+GLZX1vOz1z5lSHoC0084JtwhdY0IZOS7x8Qr3La6XbDxHXj/XnhpNvz7t3DWXTD2IuuCMqYPsv+1YeKJEu79yiQm5KTz7Wc+4qOtPTyzKRSSBrpWw9feg8sec0nk+evh4c/D6ldtkZ4xfYwliDBKiPXw6LVTGJQaz02PFbNld4TcwyEqCsZdBF9fBF9+FFoa4NmvwpyzYO1CSxSBKFkKc2fAu7+yPy8TNiFNECIyXUQ+E5H1InKXn/3Hi8gHItIkInd02rdZRFaKyHIR6f0jz900MDmOv15fQJsq1/+liL11zeEOKXiiPHDiZfCNJTDzATeV9qnL4M/nwoa37BefP43VMP9OePQcKFsOb/8cFvzA/qxMWIQsQYiIB7gfmAGMBa4QkbGdDtsDfAv4zSEuM1VVJx5qhD1SHJuVzCPXTKGksoGbHy+msSXCSnR7omHSV+GbS90NjarL4G8Xw1/Og83vhzu63mP1K3D/ybDkESi8Gb67Bk6+BRbfD69+B9rbwx2h6WdC2YIoBNar6kZVbQaeAWb6HqCq5apaBLSEMI4+oSAvg9/Pmkjxlr189+8raG+PwG+Mnhi3kvtby2DGr2HPRvjr+fDYl9wivf6qajs881V49iq3av2mN+C8X7vpw9Pvgc/dDkv/Av/4OrS1hjta04+EMkEMBbb5vC7xbguUAgtFZKmIzD7UQSIyW0SKRaS4oqKim6H2DuefOJi7ZxzPax+X8csFa8IdTuhEx7mpst9eDtN+Djs/cd1OT1wK25eFO7qe097mVqzffzKsf9Pd6Gn2O5Dj02AWgS/8BM7+IXz8DLxwA7RGUDek6dVCOc3V3xLhrnwtPl1VS0UkG/iXiKxR1fcOuqDqHNzNjJgyZUqf/9o9+4xj2ba3noff3UjugESuOmV4uEMKnZgEOO1WV1Cw6BH4z33wyFQ47nyYerdbcxGpdqyEV74N25fCiHPg/N+6KcOHcsadEJMEC+52q9xnPQ4x8T0Xr+mXQtmCKAFyfV7nAKWBnqyqpd6f5cBLuC6riCci/M+F4zj7+Gx+/PIq3lqzM9whhV5cMnzuO/Dtj2HqD9y4xEOfg+euhfIIa0k118HCH8HDZ0LlVrjkz3DVC4dPDh1O/S+44PewbiE8Nctdy5gQCmWCKAJGiUi+iMQClwPzAjlRRJJEJKXjOTANWBWySHuZaE8Uf7xiEmOHpHLrUx+xsqQq3CH1jPhUOPN7cNsK9415/RvwwCnwws3uZkd93Trv51n0Bzdo/40lbt1IVwofTrkBLn4INv8b/vZlaOyj/za2LYGlf4X6HrrDoumWkNZiEpHzgHsBDzBXVX8uIrcAqOpDInIMUAykAu1ALW7G00BcqwFcN9hTqvrzI71fX6nFFKjy6kYufmARzW3tvPRfp5EzoJ/dp6FuNyy6Dz6cA23NMOFyl0AG5IU7sq6pLYfX74JVL8DA0W4mV97pR3fNT/4BL9wIg06Aq1/qOyXZWxrhrZ/BB/cDCp5YGHOh62bM+7xVCQ4DK9bXh63dWcMlDy7imNR4nv/6aaQlxIQ7pJ5XWw7v/x6K/gzaBpOuci2MtJxwR3Z47e3w0ePwrx+7xYKfvwM+d5sbpA+GtQvg2avdnQOveRmSs4Nz3VAp+xhe+por+jjlBvf3uOJZN/jeWOVqeJ10DUz8au//LBHEEkQft2jDLq6du4QpwzN47IZCYqP76QL46lJX32mpt4zH5Ovg89+FlF5Yx6riMzcIvfUDGP45uPBeGDgq+O+z8R14+gpIHQLXzHP3+eht2tvgP/fC27+AxEyY+ScYde7+/S0N8Ok81+W0dZG7adVx58Hka+HYs62OV4hZgogAL31UwneeXcGXJw3lt7MmIP25KV65Fd77NXz0pFutPXSy94ZGp0JuISQMCF9sLY0uib3/e4hNgi/+3H0jDuXf19bF8ORlkJDukkQgA949Zc9GeOkWdz+RsRe5QfbDdYdVrIVlj8GKp6F+N6QNg5Oudq2N1CE9FnZ/YgkiQvzxzXX89l9r+dbZI7l92nHhDif89myE4rnuHhVly6Hdu4gse6xLFsNOdYkjPfewlwmaTe+5Fc+718P4WfDF/4PkrJ557+3L4IkvQ3SC627KGt0z73soqq5FsOAHrkVw/m9g/GWBJ8rWJljzqmstbnoXJApGfdG1Kkaea/cbCSJLEBFCVbnrhZU8W7yNX11yIrMKeugXX1/QXO/WFGxd7Lp1ti2B5hq3LzXH28I4BYafFvw74NXvgYU/hOVPugH0C34PI84O3vUDtfMTeNxbrODqf8AxJ/R8DAA1O2HeN939QfLPhIseOLrxoj0bYdnf3J9v7U5IGeJaFCddDenDghd3P2UJIoK0tLVzw1+L+GDDbv5yfQGfH9VD31D7mrZWKP9kf8LY8gHU7nD74tJg2Mn7u6WGnNS9RWeq8PGzsOC/3SDrad+EM74HsWGcbbZrnStd0lIPV7/out960qcvwyu3uff/wk+hcHbwknFbC6x93bUq1r/hto0427UqjjvPlXIxXWYJIsLUNLZw2UMfULK3gb/fcipjBqeGO6TeTxUqt7iEscV7B7xdn7l9nliXJHzHMY40bXT3BnjtdjdInFMAF94Hg3rJ7WP3bnZJon4PfPXv7lawodZYBfO/52YkDZ4IX34ktN1cldvgoyfgo79B9XZIyoaJV7pZUJkjQve+EcgSRAQqq2rg4vsXAfDSN05jcFpCmCPqg+p2u8HTrR+4hFH6EbR760ZmjdnfJTXsFEjLdf3nrc1uodt7v3aJ5Zwfw5Qbe99Mm6rtrrupejtc/hSMmBq699r4Lvzjv9w9ys+4w01B7qlv8+1trjWx9DHXutA2t55i8nVufUWwphRHMEsQEerT0mpmPfwBOQMS+Pstp5ISb03so9LS4B3H8CaMbUugqdrtSx3qEkX5ajePf8yXYMavIHVweGM+nNpyePwiN2g+63E4bnpwr9/SAG/+Lyx+ADJHwsVzIKeHu7R8VZe5cYplj7vWYkIGTLjCdUFl2aSOQ7EEEcHeW1vB9X8t4rQRmcy9roAYTy/7JtuXtbe5ZOA7jhEd60pwHzcj3NEFpn6Pm920YyVc8iiMuzg41y1dDi/Odt10hbPdeEM4x158tbfDpndcq2LNa65VOOxUGPkFN8Mt+3hIz+t9rb4wsQQR4Z4r2sb3XviYr0zJ5Z5LxvfvNRLmYI1V8NRXXHfazPtdX313tbW6NR7v3gNJWe56I88JXqzBVlvh1lR89MT+MSeAmETXqsgaA9k+j9Sh/a7chyWIfuC3Cz/jj2+t545po7n17BCs2DV9W3MdPHOlG1Q//3dQcGPXr7FrvSuVsb0YTrgEzvtN36kBBe52rhWfQcVqb1eh99Exuw0gLhWyjnetjOyx3udjXemPCE0ch0sQttokQtx+7mhK9jbwm4VryRmQyEWTemHJBRM+sUlwxbPw92vd7KuWBncvjkCoQvGfXZlyT6wrUT7+0tDGGwrxqZBb4B6+6vdAxRrXnVi+xiWN1a+6sYwOCQO83VNj9ieN7DF9K0F2gyWICCEi/PKSEymrauDO51eQnRrHaSMGhjss05vExMNXnoAXboKFP3BrFc648/DfjKvLYN6tbqbQiLNdl1KklbxIzHCz1Yaftn+bKtRV+CSNT10S+fi5/RMXAJIH+SQMn1ZHfGRMPbcupghTVd/CJQ8tYmd1Iy9+/TRGDUoJd0imt2lrdb/0VzwNp98GX/gf/0li1Yve1kYjTPsZFNwUsd0sAVN1RSM7ZrN1tDwqPnMJt0PKENctlZjhChTue2S42VWdt4VxOq6NQfQzJXvrufiBRcR6onjpG6eRnWK3pjSdtLfD/O+6WlaFX3Mzszpm9TTshdfugFXPu5XYFz8cmkq0kaS93U2tLV/txjh2rYO6Xa7gYMMe143l2/LoLDbZTzLJ9CaTQySZ6NighG4Joh9aWVLFrIc/YGR2Ms/MPoWkOOtNNJ2ouhpSH/zJ1Ta68A+u4OA//gvqyuHM78PnbrfCeMHS2rw/WdTv9nns8W7vtK1+z/56Yv7Epe5PFum5bq1LN9ggdT80PieNP105iZsfL+bGx4r4+cXjGZGVHO6wTG8iAtP+nxvAfveXULoCdq50d727/EkYelK4I4ws0bHu3iVduX9Ja9P+hHJAEumUaFqbQxKytSAi3PNLS/jJy6tobG3nysJhfOucUWSlWPkB08n7v3erogtnuzGJGCvd0l9YF1M/t6u2iT+8uY6nPtxKXHQUXztzBDd9Pp/EWGtAGh8tDZYY+iFLEAaAjRW1/HrBZ/xz1Q6yU+K4/dzRXDo5h2grz2FMv3W4BGG/GfqRY7OSefCqybzw9VPJzUjkrhdXMuO+f/Pm6p1E0hcFY0xwWILohyYPz+D5W07loasm09au3PhYMZfPWcyKbZXhDs0Y04tYguinRITpJxzDgu+cwc8uOoENFbXMvP8/fPPpj9i6u/7IFzDGRDwbgzAA1Da1MufdDTzy7020trdz9Sl5fPPskQxICs5iHGNM7xS2MQgRmS4in4nIehG5y8/+40XkAxFpEpE7unKuCa7kuGhun3Yc79x5FpdOzuGvizZxxq/f5sF3NtDY0hbu8IwxYRCyFoSIeIC1wLlACVAEXKGqn/ockw0MBy4C9qrqbwI91x9rQQTPup01/PL1NbyxupwhafF8d9pxXDRpKJ6ofl6Lx5gIE64WRCGwXlU3qmoz8Aww0/cAVS1X1SKgpavnmtAaNSiFR68t4OmbT2FgShzf/fsKLvjj+7y3tiLcoRljekgoE8RQYJvP6xLvtqCeKyKzRaRYRIorKuyXV7CdOiKTf/zX6fzxiknUNrVwzdwlXP3nD/mktCrcoRljQiyUCcJfX0Sg/VkBn6uqc1R1iqpOycrKCjg4E7ioKOHCCUN44/Yz+fEFY1m5vYoL/vg+tz+3nO2VDeEOzxgTIqFMECVArs/rHKC0B841IRIX7eGGz+Xz7p1T+doZI3j14zKm/uYdfvHP1VQ1dO4lNMb0daFMEEXAKBHJF5FY4HJgXg+ca0IsLSGGu2Ycz9t3nMWFJw5hznsbOfPXb/PovzfS1GoznoyJFCFdByEi5wH3Ah5grqr+XERuAVDVh0TkGKAYSAXagVpgrKpW+zv3SO9ns5jC45PSKu755xr+vW4XuRkJ3HLmCL40YQgp8THhDs0YcwRWrM/0iPfWVvCrBWtYtb2axFgPX5owhMsLhzEhJw3p77eqNKaXshsGmR5xxugsPj9qICtKqnj6w628vLyUZ4q2MWZwKlcW5jJz0lBSrVVhTJ9hLQgTMjWNLcxbUcpTH27lk9Jq4mOiuODEIVxROIyThqVbq8KYXsC6mEzYrSyp4qklW5m3fDt1zW0cNyiFywtz+fKkHNISrVVhTLhYgjC9Rl1TK6+sKOXpJVtZUVJFXHQU548fzOWFwyjIG2CtCmN6mCUI0yt9UlrFM0u28Y+PtlPT1MqIrCSuKBzGJSflWBVZY3qIJQjTq9U3t/Lqx2U8s2Qry7ZWEuuJYvoJx3BF4TBOOTbDWhXGhJAlCNNnrNlRzTNLtvHishKqG1vJH5jE5QW5XDI5h4HJceEOz5iIYwnC9DmNLW3MX1nG00u2UrR5LzEeYdq4Y7iycBinHptJlJUdNyYoLEGYPm3dzhqeKdrGC8tKqKxvYVhGIpcX5nLp5ByyU+LDHZ4xfZolCBMRGlvaWPDJDp76cCsfbtpDdJRw9vHZnDE6i8L8DEZmJVvLwpguspXUJiLEx3iYOXEoMycOZUNFLc8WbePl5dtZ+OlOANITY5gyPIPC/AEU5GVwwtA0YjwhvauuMRHNWhCmT1NVtu6pZ8mmPRRt3kPR5r1s2lUHQEKMh0nD0inIy6AwP4NJw9JJjLXvRMb4shaEiVgiwvDMJIZnJnHZFHcLkfKaRoo3792XNP741jraFaKjhHFD0ygYPoCC/AwK8jLIsPUWxhyStSBMxKtubGHZlr2uhbFpL8tLKmlubQdgZHayt4XhuqVyBiSGOVpjepYNUhvjo7GljZXbq/a1MJZu3ktNUysAQ9Li97UubODb9AfWxWSMj/gYDwV5LgkAtLUra3ZUU7TJjWEs2rCbl5e7O9zawLfpz6wFYUwnqsqW3fUs2bzHmzT2sHl3PQAxHmFEVjKjBqUwOtv9PO6YFIZlJOKxlobpg6wFYUwXiAh5A5PIG5jErI6B7+pGijbv5ePtlazbWctHW/fyyorSfefERUcxIiuZ0YOSGX1MCqOzUxg9KIWcAQnWRWX6LGtBGNNNdU2trCuvZe3OGtbtrOGznbWs21lDWVXjvmMSYjyMGpTMqOyU/cljUApD0uKtCKHpFawFYUwIJMVFMzE3nYm56Qdsr25sYd1Olzhc8qjlvXUVvLCsZN8xyXHRjMxO5rhBKYwalMxob1dVdkqcJQ7Ta1iCMCbIUuNjmDx8AJOHDzhge2V9M2t9EsfanTW8sXonzxZv8zk3mtGDUtzYxqBkRmanMDI7mUGpljhMz7MuJmPCbFdt076WRsfPz3bWUNXQsu+Y5LhoRmQlMSI7mZHZyYzIcj+HZSTarCpzVKyLyZhebGByHAOT4zhtxMB921SVipom1pfXsqGilvXltayvqGXR+t28uGz7vuNiPG4l+cisZEZkJ+1LHiOykkmKs//e5ujYvyBjeiERITs1nuzUeE4bOfCAfTWNLWyoqGODN2msL69lbXkN/1q9k7b2/T0CQ9LiGeHT2uj4OTA51rqrTEBCmiBEZDpwH+ABHlXVezrtF+/+84B64DpVXebdtxmoAdqA1kM1gYzpb1LiY/wOjje3trN1T51rbZTXsqHCPX+ueBv1zW37jktLiGFEVtIBSWNEVjK5tpbDdBKyBCEiHuB+4FygBCgSkXmq+qnPYTOAUd7HycCD3p8dpqrqrlDFaEwkiY2O8g5qpxywXVUpq2o8sLuqvJa31lTwXPH+mVWxniiyU+M4JjWeQd7HMWlx+5+nxnNMWjzxMZ6e/mgmTELZgigE1qvqRgAReQaYCfgmiJnA4+pGyheLSLqIDFbVshDGZUy/IiIMSU9gSHoCZ4zOOmBfVX0L6ytq2VBey8ZddeyoamBndROry6p5+7PyA1oeHdISYhiUGndA0vBNIoPS4hiYFGcLBCNAKBPEUGCbz+sSDmwdHOqYoUAZoMBCEVHgYVWd4+9NRGQ2MBtg2LBhwYncmH4iLdH/lFxwLY+aplbKqxvZUdXEjupGdnofO6rcz7U7a6ioaaK902TI6CghK+XAJNLROnFJxP20gfTeLZR/O/6+PnSeU3u4Y05X1VIRyQb+JSJrVPW9gw52iWMOuGmuRxOwMWY/ESE1PobU+JiDuq18tbUru2qb9iWNndWN7PAmlfKaRjZU1PKfDbuoaWw96NyU+GiGpCVwTFo8g9PiGZyWwOC0+P2v0xNItiQSNqH8ky8Bcn1e5wClgR6jqh0/y0XkJVyX1UEJwhgTXp4o2dfFdDj1za3srN6fSMr2/WygrKqRT8uq2VXbROelWSlx0S5hpCcwOPXA5NGRTFLjY0L4CfuvUCaIImCUiOQD24HLgSs7HTMPuNU7PnEyUKWqZSKSBESpao33+TTgf0MYqzEmxBJjo8kfGE3+wKRDHtPc2r6vBVJW1UhZpUseO6pcIllTVk2FnySS3JFEvF1Xvsmjo2WSGh9t03u7KGQJQlVbReRWYAFumutcVf1ERG7x7n8ImI+b4roeN831eu/pg4CXvH+Z0cBTqvp6qGI1xvQOsdFR5GYkkptx6Dv7tbS17xsH6UgepVUN+16v3VlBec3BSUTEFU9MjI0mMdbj84gmwed5YqfnHfuSDnFcQqyHWE9URCYfK7VhjIk4LW3tVNQ07eu+2lHVSHVDC3XNbdQ3t9HQ3Eq993n9Ac+9+1raDkowhxMdJQckksQ4D2kJMaQnxJKeGOMeCbGkJcaQnhDDgKRY0hNivK9jiY0OX7kUK7VhjOlXYjxR+6b2doeq0tjS3il5tNLQ3OZNMvuf+0s2dU2tVDW0UFZVTVV9C5UNLQescu8sKdZDemKsSyodCSXRJZGO5LJvmzfJpCXGEBcd2jUpliCMMaYTEdciSIj1kBmE63VMGa6qb6GyvoXKhmb3s977s8Ftr2poZm99C5/tcMUaK+tbaD1MYkmM9ZCeEEPOgESeu+XUIER6IEsQxhgTYr5ThnMzAj9PValrbmNvXfO+hOEvuUSHaFGiJQhjjOmlRITkuGiS46IPWA/QU6yQvDHGGL8sQRhjjPHLEoQxxhi/LEEYY4zxyxKEMcYYvyxBGGOM8csShDHGGL8sQRhjjPEroor1iUgFsKWbpw8EIvX+1/bZ+q5I/nz22XqH4aqa5W9HRCWIoyEixYeqaNjX2WfruyL589ln6/2si8kYY4xfliCMMcb4ZQlivznhDiCE7LP1XZH8+eyz9XI2BmGMMcYva0EYY4zxyxKEMcYYv/p9ghCR6SLymYisF5G7wh1PMIlIroi8LSKrReQTEfl2uGMKNhHxiMhHIvJquGMJJhFJF5HnRWSN9+8v+PeTDCMR+Y733+QqEXlaROLDHVN3ichcESkXkVU+2zJE5F8iss77c0A4Y+yufp0gRMQD3A/MAMYCV4jI2PBGFVStwHdVdQxwCvCNCPt8AN8GVoc7iBC4D3hdVY8HJhBBn1FEhgLfAqao6gmAB7g8vFEdlb8C0zttuwt4U1VHAW96X/c5/TpBAIXAelXdqKrNwDPAzDDHFDSqWqaqy7zPa3C/ZIaGN6rgEZEc4Hzg0XDHEkwikgqcAfwZQFWbVbUyrEEFXzSQICLRQCJQGuZ4uk1V3wP2dNo8E3jM+/wx4KKejClY+nuCGAps83ldQgT9AvUlInnAJODDMIcSTPcC3wPawxxHsB0LVAB/8XafPSoiSeEOKlhUdTvwG2ArUAZUqerC8EYVdINUtQzcFzUgO8zxdEt/TxDiZ1vEzfsVkWTgBeA2Va0OdzzBICIXAOWqujTcsYRANHAS8KCqTgLq6KNdFP54++NnAvnAECBJRK4Kb1TGn/6eIEqAXJ/XOfThpq4/IhKDSw5PquqL4Y4niE4HviQim3Fdg2eLyBPhDSloSoASVe1o7T2PSxiR4gvAJlWtUNUW4EXgtDDHFGw7RWQwgPdneZjj6Zb+niCKgFEiki8isbiBsnlhjiloRERw/dirVfV34Y4nmFT1blXNUdU83N/bW6oaEd9CVXUHsE1EjvNuOgf4NIwhBdtW4BQRSfT+Gz2HCBqE95oHXOt9fi3wchhj6bbocAcQTqraKiK3AgtwMynmquonYQ4rmE4HrgZWishy77b/VtX54QvJBOibwJPeLy4bgevDHE/QqOqHIvI8sAw30+4j+nBpChF5GjgLGCgiJcBPgHuA50TkRlxCvCx8EXafldowxhjjV3/vYjLGGHMIliCMMcb4ZQnCGGOMX5YgjDHG+GUJwhhjjF+WIIzpBUTkrEirSGv6PksQxhhj/LIEYUwXiMhVIrJERJaLyMPe+1HUishvRWSZiLwpIlneYyeKyGIR+VhEXuq4J4CIjBSRN0RkhfecEd7LJ/vcA+JJ7ypjY8LGEoQxARKRMcBXgNNVdSLQBnwVSAKWqepJwLu4lbQAjwPfV9UTgZU+258E7lfVCbgaRGXe7ZOA23D3JjkWtxLemLDp16U2jOmic4DJQJH3y30CrghbO/Cs95gngBdFJA1IV9V3vdsfA/4uIinAUFV9CUBVGwG811uiqiXe18uBPOD9kH8qYw7BEoQxgRPgMVW9+4CNIj/qdNzh6tccrtuoyed5G/b/04SZdTEZE7g3gUtFJBv23Xd4OO7/0aXeY64E3lfVKmCviHzeu/1q4F3v/ThKROQi7zXiRCSxJz+EMYGybyjGBEhVPxWRHwILRSQKaAG+gbuhzzgRWQpU4cYpwJV5fsibAHwrsl4NPCwi/+u9Rp+s9Gkin1VzNeYoiUitqiaHOw5jgs26mIwxxvhlLQhjjDF+WQvCGGOMX5YgjDHG+GUJwhhjjF+WIIwxxvhlCcIYY4xf/x93ajF3Rl6V6wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# summarize history for accuracy\n",
+    "plt.plot(history.history['accuracy'])\n",
+    "plt.plot(history.history['val_accuracy'])\n",
+    "plt.title('Model accuracy')\n",
+    "plt.ylabel('accuracy')\n",
+    "plt.xlabel('epoch')\n",
+    "plt.legend(['train', 'test'], loc='upper left')\n",
+    "plt.show()\n",
+    "\n",
+    "# summarize history for loss\n",
+    "plt.plot(history.history['loss'])\n",
+    "plt.plot(history.history['val_loss'])\n",
+    "plt.title('Model loss')\n",
+    "plt.ylabel('loss')\n",
+    "plt.xlabel('epoch')\n",
+    "plt.legend(['train', 'test'], loc='upper left')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:45:58.058095Z",
+     "iopub.status.busy": "2022-08-06T04:45:58.056779Z",
+     "iopub.status.idle": "2022-08-06T04:46:06.933957Z",
+     "shell.execute_reply": "2022-08-06T04:46:06.932903Z",
+     "shell.execute_reply.started": "2022-08-06T04:45:58.058048Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "685/685 [==============================] - 5s 8ms/step - loss: 0.1169 - accuracy: 0.9723\n",
+      "Test loss: 0.1168890967965126\n",
+      "Test accuracy: 0.9722729921340942\n",
+      "685/685 [==============================] - 5s 7ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "loss,accuracy = Model.evaluate(X_tests,Y_tests,verbose=1)\n",
+    "\n",
+    "print('Test loss:', loss)\n",
+    "print('Test accuracy:', accuracy)\n",
+    "\n",
+    "y_predict = Model.predict(X_tests)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:46:06.936272Z",
+     "iopub.status.busy": "2022-08-06T04:46:06.935383Z",
+     "iopub.status.idle": "2022-08-06T04:46:06.946771Z",
+     "shell.execute_reply": "2022-08-06T04:46:06.945667Z",
+     "shell.execute_reply.started": "2022-08-06T04:46:06.936237Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import itertools\n",
+    "def plot_confusion_matrix(cm, classes,\n",
+    "                          normalize=False,\n",
+    "                          title='Confusion matrix',\n",
+    "                          cmap=plt.cm.Blues):\n",
+    "   \n",
+    "    if normalize:\n",
+    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
+    "        print(\"Normalized confusion matrix\")\n",
+    "    else:\n",
+    "        print('Confusion matrix, without normalization')\n",
+    "\n",
+    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
+    "    plt.title(title)\n",
+    "    plt.colorbar()\n",
+    "    tick_marks = np.arange(len(classes))\n",
+    "    plt.xticks(tick_marks, classes, rotation=45)\n",
+    "    plt.yticks(tick_marks, classes)\n",
+    "\n",
+    "    fmt = '.2f' if normalize else 'd'\n",
+    "    thresh = cm.max() / 2.\n",
+    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
+    "        plt.text(j, i, format(cm[i, j], fmt),\n",
+    "                 horizontalalignment=\"center\",\n",
+    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.ylabel('True label')\n",
+    "    plt.xlabel('Predicted label')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:46:06.949165Z",
+     "iopub.status.busy": "2022-08-06T04:46:06.948545Z",
+     "iopub.status.idle": "2022-08-06T04:46:07.357815Z",
+     "shell.execute_reply": "2022-08-06T04:46:07.356654Z",
+     "shell.execute_reply.started": "2022-08-06T04:46:06.949130Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normalized confusion matrix\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "plt.figure(figsize=(9, 9))\n",
+    "cf_matrix = confusion_matrix(Y_tests.argmax(axis=1),y_predict.argmax(axis=1))\n",
+    "plot_confusion_matrix(cf_matrix, \n",
+    "                      classes=['Normal beats','Unknown beats','Ventricular ectopic beats','Supraventricular ectopic beats','Fusion beats'],\n",
+    "                      normalize=True,\n",
+    "                      title='Confusion matrix, with normalization')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:46:07.360618Z",
+     "iopub.status.busy": "2022-08-06T04:46:07.359594Z",
+     "iopub.status.idle": "2022-08-06T04:46:07.437053Z",
+     "shell.execute_reply": "2022-08-06T04:46:07.435896Z",
+     "shell.execute_reply.started": "2022-08-06T04:46:07.360569Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "Model.save('./ecg_model.h5')\n",
+    "Model.save_weights('./ecg_weights.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Performing prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:46:07.439019Z",
+     "iopub.status.busy": "2022-08-06T04:46:07.438631Z",
+     "iopub.status.idle": "2022-08-06T04:46:07.446045Z",
+     "shell.execute_reply": "2022-08-06T04:46:07.444280Z",
+     "shell.execute_reply.started": "2022-08-06T04:46:07.438975Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def printPrediction(max_value):\n",
+    "    if(max_value==0):\n",
+    "        print(\"Non-Ectopic Beats\")\n",
+    "    elif(max_value==1):\n",
+    "        print(\"Supraventricular Ectopic Beats\")\n",
+    "    elif(max_value==2):\n",
+    "        print(\"Ventricular Ectopic Beats\")\n",
+    "    elif(max_value==3):\n",
+    "        print(\"Fusion Beats\")\n",
+    "    else:\n",
+    "        print(\"Unknown Beats\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:46:07.448703Z",
+     "iopub.status.busy": "2022-08-06T04:46:07.447622Z",
+     "iopub.status.idle": "2022-08-06T04:46:07.521042Z",
+     "shell.execute_reply": "2022-08-06T04:46:07.520113Z",
+     "shell.execute_reply.started": "2022-08-06T04:46:07.448665Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 16ms/step\n",
+      "[0.002719925483688712, 0.9972800016403198, 6.1388143457463684e-09, 1.92232962709061e-15, 2.951333066825157e-10]\n",
+      "Supraventricular Ectopic Beats\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_list = []\n",
+    "prediction  = Model.predict(np.array([X_train[25000]])).tolist()\n",
+    "\n",
+    "for it in prediction:\n",
+    "    for el in it:\n",
+    "        my_list.append(el)\n",
+    "        \n",
+    "print(my_list)        \n",
+    "max_value = my_list.index(max(my_list))\n",
+    "\n",
+    "if(max_value==0):\n",
+    "    print(\"Normal Beats\")\n",
+    "elif(max_value==1):\n",
+    "    print(\"Supraventricular Ectopic Beats\")\n",
+    "elif(max_value==2):\n",
+    "    print(\"Ventricular Ectopic Beats\")\n",
+    "elif(max_value==3):\n",
+    "    print(\"Fusion Beats\")\n",
+    "else:\n",
+    "    print(\"Unknown Beats\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ECG classification with noise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train Neural Networks With Noise to Reduce Overfitting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T04:46:07.523127Z",
+     "iopub.status.busy": "2022-08-06T04:46:07.522545Z",
+     "iopub.status.idle": "2022-08-06T04:46:09.046897Z",
+     "shell.execute_reply": "2022-08-06T04:46:09.045706Z",
+     "shell.execute_reply.started": "2022-08-06T04:46:07.523094Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "gauss_std=0.5\n",
+    "gauss_k=0.1\n",
+    "\n",
+    "# Gaussian noise in train_df\n",
+    "x_noise_train = train_df.iloc[:,:186].values.copy()\n",
+    "for i in range(len(x_noise_train)):\n",
+    "    x_noise_train[i,:186]=x_noise_train[i,:186]+gauss_k*np.random.normal(0,gauss_std,186)\n",
+    "\n",
+    "x_noise_train_graph=x_noise_train\n",
+    "x_noise_train = x_noise_train.reshape(\n",
+    "    len(x_noise_train),\n",
+    "    x_noise_train.shape[1],1)\n",
+    "\n",
+    "# Gaussian noise in tests_df\n",
+    "x_noise_tests = tests_df.iloc[:,:186].values.copy()\n",
+    "for i in range(len(x_noise_tests)):\n",
+    "    x_noise_tests[i,:186]=x_noise_tests[i,:186]+gauss_k*np.random.normal(0,gauss_std,186)\n",
+    "\n",
+    "x_noise_tests_graph=x_noise_tests\n",
+    "x_noise_tests = x_noise_tests.reshape(\n",
+    "    len(x_noise_tests),\n",
+    "    x_noise_tests.shape[1],1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T05:02:00.857894Z",
+     "iopub.status.busy": "2022-08-06T05:02:00.857305Z",
+     "iopub.status.idle": "2022-08-06T05:02:33.938729Z",
+     "shell.execute_reply": "2022-08-06T05:02:33.937414Z",
+     "shell.execute_reply.started": "2022-08-06T05:02:00.857851Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2500/2500 [==============================] - 17s 7ms/step - loss: 2.3764 - accuracy: 0.6757\n",
+      "Noise Test loss: 2.3764209747314453\n",
+      "Noise Test accuracy: 0.6756500005722046\n",
+      "2500/2500 [==============================] - 17s 7ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "loss,accuracy = Model.evaluate(x_noise_train,Y_train,verbose=1)\n",
+    "\n",
+    "print('Noise Test loss:', loss)\n",
+    "print('Noise Test accuracy:', accuracy)\n",
+    "\n",
+    "y_noise_predict = Model.predict(x_noise_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T05:02:33.941409Z",
+     "iopub.status.busy": "2022-08-06T05:02:33.941024Z",
+     "iopub.status.idle": "2022-08-06T05:02:34.264475Z",
+     "shell.execute_reply": "2022-08-06T05:02:34.262938Z",
+     "shell.execute_reply.started": "2022-08-06T05:02:33.941354Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normalized confusion matrix\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(9, 9))\n",
+    "\n",
+    "cf_matrix_noise = confusion_matrix(Y_train.argmax(axis=1),y_noise_predict.argmax(axis=1))\n",
+    "plot_confusion_matrix(cf_matrix_noise,\n",
+    "                      classes=['Normal beats','Unknown beats','Ventricular ectopic beats','Supraventricular ectopic beats','Fusion beats'],\n",
+    "                      normalize=True,\n",
+    "                      title='Confusion matrix, with normalization')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T05:02:34.266205Z",
+     "iopub.status.busy": "2022-08-06T05:02:34.265799Z",
+     "iopub.status.idle": "2022-08-06T05:02:34.717183Z",
+     "shell.execute_reply": "2022-08-06T05:02:34.716263Z",
+     "shell.execute_reply.started": "2022-08-06T05:02:34.266169Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(train_df.iloc[0,:186], color='red')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T05:02:34.719885Z",
+     "iopub.status.busy": "2022-08-06T05:02:34.719259Z",
+     "iopub.status.idle": "2022-08-06T05:02:34.905752Z",
+     "shell.execute_reply": "2022-08-06T05:02:34.904456Z",
+     "shell.execute_reply.started": "2022-08-06T05:02:34.719825Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x_noise_train_graph[0], color='red')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Applying Transfer Learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T05:02:44.180744Z",
+     "iopub.status.busy": "2022-08-06T05:02:44.179517Z",
+     "iopub.status.idle": "2022-08-06T05:02:44.462438Z",
+     "shell.execute_reply": "2022-08-06T05:02:44.461039Z",
+     "shell.execute_reply.started": "2022-08-06T05:02:44.180684Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " conv1d (Conv1D)             (None, 184, 128)          512       \n",
+      "                                                                 \n",
+      " batch_normalization (BatchN  (None, 184, 128)         512       \n",
+      " ormalization)                                                   \n",
+      "                                                                 \n",
+      " max_pooling1d (MaxPooling1D  (None, 92, 128)          0         \n",
+      " )                                                               \n",
+      "                                                                 \n",
+      " conv1d_1 (Conv1D)           (None, 90, 64)            24640     \n",
+      "                                                                 \n",
+      " batch_normalization_1 (Batc  (None, 90, 64)           256       \n",
+      " hNormalization)                                                 \n",
+      "                                                                 \n",
+      " max_pooling1d_1 (MaxPooling  (None, 45, 64)           0         \n",
+      " 1D)                                                             \n",
+      "                                                                 \n",
+      " conv1d_2 (Conv1D)           (None, 44, 64)            8256      \n",
+      "                                                                 \n",
+      " batch_normalization_2 (Batc  (None, 44, 64)           256       \n",
+      " hNormalization)                                                 \n",
+      "                                                                 \n",
+      " max_pooling1d_2 (MaxPooling  (None, 22, 64)           0         \n",
+      " 1D)                                                             \n",
+      "                                                                 \n",
+      " conv1d_3 (Conv1D)           (None, 21, 64)            8256      \n",
+      "                                                                 \n",
+      " flatten (Flatten)           (None, 1344)              0         \n",
+      "                                                                 \n",
+      " dense (Dense)               (None, 128)               172160    \n",
+      "                                                                 \n",
+      " dropout (Dropout)           (None, 128)               0         \n",
+      "                                                                 \n",
+      " dense_1 (Dense)             (None, 64)                8256      \n",
+      "                                                                 \n",
+      " dense_2 (Dense)             (None, 5)                 325       \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 223,429\n",
+      "Trainable params: 222,917\n",
+      "Non-trainable params: 512\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import load_model\n",
+    "\n",
+    "Model_transfer = load_model('./ecg_model.h5')\n",
+    "Model_transfer.load_weights('./ecg_weights.h5')\n",
+    "\n",
+    "Model_transfer.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T05:02:47.643870Z",
+     "iopub.status.busy": "2022-08-06T05:02:47.643042Z",
+     "iopub.status.idle": "2022-08-06T05:02:47.650708Z",
+     "shell.execute_reply": "2022-08-06T05:02:47.649585Z",
+     "shell.execute_reply.started": "2022-08-06T05:02:47.643822Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "Model_transfer.trainable = False\n",
+    "Model_transfer.pop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T05:03:02.684521Z",
+     "iopub.status.busy": "2022-08-06T05:03:02.683119Z",
+     "iopub.status.idle": "2022-08-06T05:03:02.796992Z",
+     "shell.execute_reply": "2022-08-06T05:03:02.795493Z",
+     "shell.execute_reply.started": "2022-08-06T05:03:02.684469Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_1\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " sequential (Sequential)     (None, 64)                223104    \n",
+      "                                                                 \n",
+      " dense_3 (Dense)             (None, 64)                4160      \n",
+      "                                                                 \n",
+      " dropout_1 (Dropout)         (None, 64)                0         \n",
+      "                                                                 \n",
+      " dense_4 (Dense)             (None, 5)                 325       \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 227,589\n",
+      "Trainable params: 4,485\n",
+      "Non-trainable params: 223,104\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "Model2 = Sequential()\n",
+    "Model2.add(Model_transfer)\n",
+    "Model2.add(Dense(64, activation='relu'))\n",
+    "Model2.add(Dropout(0.25))\n",
+    "Model2.add(Dense(5, activation='softmax'))\n",
+    "\n",
+    "Model2.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T05:03:08.022155Z",
+     "iopub.status.busy": "2022-08-06T05:03:08.021733Z",
+     "iopub.status.idle": "2022-08-06T05:03:08.034978Z",
+     "shell.execute_reply": "2022-08-06T05:03:08.033250Z",
+     "shell.execute_reply.started": "2022-08-06T05:03:08.022122Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "Model2.compile(\n",
+    "    loss='categorical_crossentropy',\n",
+    "    optimizer='adam',metrics=['accuracy']\n",
+    ")\n",
+    "\n",
+    "callbacks2 = [\n",
+    "    EarlyStopping(monitor='val_loss', patience=8), \n",
+    "    ModelCheckpoint(filepath='./best_noise_weights.h5', monitor='val_loss', save_best_only=True)\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T05:03:16.606028Z",
+     "iopub.status.busy": "2022-08-06T05:03:16.605592Z",
+     "iopub.status.idle": "2022-08-06T05:06:51.598414Z",
+     "shell.execute_reply": "2022-08-06T05:06:51.597426Z",
+     "shell.execute_reply.started": "2022-08-06T05:03:16.605995Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "2500/2500 [==============================] - 22s 9ms/step - loss: 0.6552 - accuracy: 0.7815 - val_loss: 0.6678 - val_accuracy: 0.7633\n",
+      "Epoch 2/10\n",
+      "2500/2500 [==============================] - 22s 9ms/step - loss: 0.5050 - accuracy: 0.8170 - val_loss: 0.5972 - val_accuracy: 0.7745\n",
+      "Epoch 3/10\n",
+      "2500/2500 [==============================] - 22s 9ms/step - loss: 0.4892 - accuracy: 0.8225 - val_loss: 0.6029 - val_accuracy: 0.7705\n",
+      "Epoch 4/10\n",
+      "2500/2500 [==============================] - 22s 9ms/step - loss: 0.4834 - accuracy: 0.8237 - val_loss: 0.6961 - val_accuracy: 0.7173\n",
+      "Epoch 5/10\n",
+      "2500/2500 [==============================] - 22s 9ms/step - loss: 0.4779 - accuracy: 0.8262 - val_loss: 0.7023 - val_accuracy: 0.7148\n",
+      "Epoch 6/10\n",
+      "2500/2500 [==============================] - 22s 9ms/step - loss: 0.4760 - accuracy: 0.8258 - val_loss: 0.6842 - val_accuracy: 0.7386\n",
+      "Epoch 7/10\n",
+      "2500/2500 [==============================] - 22s 9ms/step - loss: 0.4731 - accuracy: 0.8265 - val_loss: 0.5341 - val_accuracy: 0.8012\n",
+      "Epoch 8/10\n",
+      "2500/2500 [==============================] - 22s 9ms/step - loss: 0.4724 - accuracy: 0.8272 - val_loss: 0.7428 - val_accuracy: 0.7015\n",
+      "Epoch 9/10\n",
+      "2500/2500 [==============================] - 22s 9ms/step - loss: 0.4690 - accuracy: 0.8283 - val_loss: 0.6462 - val_accuracy: 0.7540\n",
+      "Epoch 10/10\n",
+      "2500/2500 [==============================] - 22s 9ms/step - loss: 0.4721 - accuracy: 0.8273 - val_loss: 0.6804 - val_accuracy: 0.7418\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x1bd7bb18f40>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Model2.fit(x_noise_train,Y_train,\n",
+    "           epochs=10, callbacks=callbacks2, \n",
+    "           batch_size=32, validation_data=(x_noise_tests,Y_tests))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Saving the Model2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T05:06:51.602747Z",
+     "iopub.status.busy": "2022-08-06T05:06:51.602321Z",
+     "iopub.status.idle": "2022-08-06T05:06:51.658120Z",
+     "shell.execute_reply": "2022-08-06T05:06:51.656861Z",
+     "shell.execute_reply.started": "2022-08-06T05:06:51.602712Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "Model2.save('./best_noise_model.h5')\n",
+    "Model2.load_weights('./best_noise_weights.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Performing Fine Turning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T05:07:50.937430Z",
+     "iopub.status.busy": "2022-08-06T05:07:50.935899Z",
+     "iopub.status.idle": "2022-08-06T05:18:37.437138Z",
+     "shell.execute_reply": "2022-08-06T05:18:37.435958Z",
+     "shell.execute_reply.started": "2022-08-06T05:07:50.937346Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "2500/2500 [==============================] - 84s 33ms/step - loss: 0.2122 - accuracy: 0.9276 - val_loss: 0.2654 - val_accuracy: 0.9088\n",
+      "Epoch 2/10\n",
+      "2500/2500 [==============================] - 84s 34ms/step - loss: 0.1465 - accuracy: 0.9498 - val_loss: 0.2438 - val_accuracy: 0.9131\n",
+      "Epoch 3/10\n",
+      "2500/2500 [==============================] - 89s 36ms/step - loss: 0.1233 - accuracy: 0.9576 - val_loss: 0.2362 - val_accuracy: 0.9141\n",
+      "Epoch 4/10\n",
+      "2500/2500 [==============================] - 83s 33ms/step - loss: 0.1119 - accuracy: 0.9613 - val_loss: 0.2240 - val_accuracy: 0.9180\n",
+      "Epoch 5/10\n",
+      "2500/2500 [==============================] - 83s 33ms/step - loss: 0.1009 - accuracy: 0.9659 - val_loss: 0.2078 - val_accuracy: 0.9239\n",
+      "Epoch 6/10\n",
+      "2500/2500 [==============================] - 85s 34ms/step - loss: 0.0924 - accuracy: 0.9679 - val_loss: 0.2088 - val_accuracy: 0.9250\n",
+      "Epoch 7/10\n",
+      "2500/2500 [==============================] - 85s 34ms/step - loss: 0.0873 - accuracy: 0.9703 - val_loss: 0.2060 - val_accuracy: 0.9262\n",
+      "Epoch 8/10\n",
+      "2500/2500 [==============================] - 87s 35ms/step - loss: 0.0801 - accuracy: 0.9719 - val_loss: 0.1900 - val_accuracy: 0.9344\n",
+      "Epoch 9/10\n",
+      "2500/2500 [==============================] - 87s 35ms/step - loss: 0.0768 - accuracy: 0.9736 - val_loss: 0.2030 - val_accuracy: 0.9281\n",
+      "Epoch 10/10\n",
+      "2500/2500 [==============================] - 87s 35ms/step - loss: 0.0736 - accuracy: 0.9749 - val_loss: 0.1959 - val_accuracy: 0.9325\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x1bd7bd11d90>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.optimizers import Adam\n",
+    "\n",
+    "callbacks3 = [\n",
+    "    EarlyStopping(monitor='val_loss', patience=8), \n",
+    "    ModelCheckpoint(filepath='./best_noise_weights2.h5', monitor='val_loss', save_best_only=True)\n",
+    "]\n",
+    "\n",
+    "Model_transfer.trainable = True\n",
+    "\n",
+    "Model2.compile(\n",
+    "    loss='categorical_crossentropy',\n",
+    "    optimizer=Adam(0.0001),\n",
+    "    metrics=['accuracy']\n",
+    ")\n",
+    "\n",
+    "Model2.fit(x_noise_train,Y_train, \n",
+    "           epochs=10,callbacks=callbacks3, \n",
+    "           batch_size=32, validation_data=(x_noise_tests,Y_tests))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Confusion matrix for noisy signals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T05:25:46.406761Z",
+     "iopub.status.busy": "2022-08-06T05:25:46.406054Z",
+     "iopub.status.idle": "2022-08-06T05:25:51.029148Z",
+     "shell.execute_reply": "2022-08-06T05:25:51.027792Z",
+     "shell.execute_reply.started": "2022-08-06T05:25:46.406710Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "685/685 [==============================] - 5s 7ms/step\n",
+      "Normalized confusion matrix\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_noise_predict2 = Model2.predict(x_noise_tests)\n",
+    "\n",
+    "plt.figure(figsize=(9,9))\n",
+    "cf_matrix_noise2 = confusion_matrix(Y_tests.argmax(axis=1),y_noise_predict2.argmax(axis=1))\n",
+    "plot_confusion_matrix(cf_matrix_noise2, \n",
+    "                      classes=['Normal beats','Unknown beats','Ventricular ectopic beats','Supraventricular ectopic beats','Fusion beats'],\n",
+    "                      normalize=True,\n",
+    "                      title='Confusion matrix, with normalization')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Confusion matrix for preprocessed signals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-06T05:26:31.780168Z",
+     "iopub.status.busy": "2022-08-06T05:26:31.779731Z",
+     "iopub.status.idle": "2022-08-06T05:26:36.021845Z",
+     "shell.execute_reply": "2022-08-06T05:26:36.021026Z",
+     "shell.execute_reply.started": "2022-08-06T05:26:31.780135Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "685/685 [==============================] - 5s 7ms/step\n",
+      "Normalized confusion matrix\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_predict2 = Model2.predict(X_tests)\n",
+    "\n",
+    "plt.figure(figsize=(9,9))\n",
+    "cf_matrix2 = confusion_matrix(Y_tests.argmax(axis=1),y_predict2.argmax(axis=1))\n",
+    "plot_confusion_matrix(cf_matrix2, \n",
+    "                      classes=['Normal beats','Unknown beats','Ventricular ectopic beats','Supraventricular ectopic beats','Fusion beats'],\n",
+    "                      normalize=True,\n",
+    "                      title='Confusion matrix, with normalization')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Heartbeat Classification using ECG/requirements.txt b/Heartbeat Classification using ECG/requirements.txt
new file mode 100644
index 00000000..2f19ac87
--- /dev/null
+++ b/Heartbeat Classification using ECG/requirements.txt	
@@ -0,0 +1,8 @@
+Required packages/libraries to run the project in other machines:
+
+numpy==1.23.1
+pandas==1.4.3
+matplotlib==3.1
+keras==2.9.0
+sklearn==0.20
+tensorflow==2.9.0