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This page lists scientific research publications that have used hctsa. |
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Articles are labeled as follows:
- 📗 = Journal publication.
- 📙 = Preprint.
- 💻 = Link to GitHub code repository available.
If you have used hctsa in your published work, or we have missed any publications, feel free to reach out by email and we'll add it this growing list!
The following publications for details of how the highly-comparative approach to time-series analysis has developed since our initial publication in 2013. We:
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We have used hctsa to:
as well as:
- Connect structural brain connectivity to fMRI dynamics (mouse).
- Connect structural brain connectivity to fMRI dynamics (human).
- Distinguish time-series patterns for data-mining applications.
- Classify babies with low blood pH from fetal heart rate time series.
Confirm the role of µORs in VTA and NAc in acute fentanyl-induced behaviour (positive reinforcement). | https://www.nature.com/articles/s41586-024-07440-x | Screenshot 2024-05-28 at 10.30.28.png |
Assess stress-induced changes in astrocyte calcium dynamics. | https://www.nature.com/articles/s41467-020-15778-9 | Screenshot 2024-05-10 at 2.56.37 pm.png |
Assess the stress controllability of neurons from their activity time series. | https://www.nature.com/articles/s41593-020-0591-0 | Screenshot 2024-05-10 at 3.03.50 pm.png |
Here are some highlights:
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In addition to:
- Predict age from resting-state MEG from individual brain regions.
- Estimate brain age in children from EEG.
- Extract gradients from fMRI hctsa time-series features to understand the relationship between schizophrenia and nicotine dependence.
- Classify endogenous (preictal), interictal, and seizure-like (ictal) activity from local field potentials (LFPs) from layers II/III of the primary somatosensory cortex of young mice (using feature selection methods from an initial pool of _hctsa_features).
- Distinguish motor-evoked potentials corresponding to multiple sclerosis.
Here are some highlights:
in addition to:
- Identify sepsis in very low birth weight (<1.5kg) infants from heart rate signals, identifying heart rate characteristics of reduced variability and transient decelerations.
- Identify novel heart-rate variability metrics, including
RobustSD, to create a parsimonious model for cerebral palsy prediction in preterm neonatal intensive care unit patients. - Predicting post cardiac arrest outcomes.
- Detect falls from wearable sensor data.
- Detect falls from wearable sensor data.
- Select features for fetal heart rate analysis using genetic algorithms.
Screen for COVID-19 using digital holographic microscopy. | getimagev2.cfm.jpeg.png | https://opg.optica.org/boe/abstract.cfm?uri=boe-13-10-5377 |
Detect COVID-19 from red blood cells using digital holographic microscopy. | getimagev2.cfm-4.jpeg | https://opg.optica.org/oe/fulltext.cfm?uri=oe-30-2-1723&id=467318 |
Identify the biogeographic heterogeneity of mucus, lumen, and feces. | Screenshot 2024-05-10 at 3.56.32 pm.png | https://www.pnas.org/doi/full/10.1073/pnas.2019336118 |
Here are some highlights:
in addition to:
- Diagnose a spacecraft propulsion system utilizing data provided by the Prognostics and Health Management (PHM) society, as part of the Asia-Pacific PHM conference’s data challenge, 2023.
- Identify faults in a large-scale industrial process.
Detecting earthquakes from seismic recordings. | gpr13386-fig-0001-m.jpg | https://onlinelibrary.wiley.com/doi/10.1111/1365-2478.13386 |
Find temporal patterns for reconstructing surface soil moisture time series. | 1-s2.0-S0022169423005218-gr13 (1).jpg | https://www.sciencedirect.com/science/article/pii/S0022169423005218?via%3Dihub |
Predict earthquakes (in the following month) from seismic indicators in Bangladesh. | ander11-3071400-large.gif | https://ieeexplore.ieee.org/document/9395582 |
Detect earthquakes in Groningen, The Netherlands. 📗 82nd EAGE Annual Conference & Exhibition Workshop Programme (2020). | Screenshot 2024-05-10 at 4.08.43 pm.png | https://www.earthdoc.org/content/papers/10.3997/2214-4609.202011128 |
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