Publications using hctsa
This page lists scientific research publications that have used hctsa
Where journal articles (📗) are not open access, we also provide a link to the preprint (📙). Links to Github code repositories (:octocat:) are provided where possible.
The following publications for details of how the highly-comparative approach to time-series analysis has developed since our initial publication in 2013:
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We reduced the hctsa feature library down to a reduced set of 22 efficiently coded features: catch22.
- 📗 CAnonical Time-series CHaracteristics. Data Mining and Knowledge Discovery 33, 1821 (2019).
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catch22 Code.
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We developed a software package for highly-comparative time-series analysis, hctsa (includes applications to high throughput phenotyping of C. Elegans and Drosophila movement time series).
- 📗 hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction. Cell Systems 5, 527 (2017).
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Code (fly) &
Code (worm).
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Introduction to using features for time-series analysis
- 📗 Feature-based time-series analysis. Feature Engineering for Machine Learning and Data Analytics, CRC Press (2018).
- 📙 Preprint.
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The behavior of thousands of time-series methods on thousands of different time series can be used to organize an interdisciplinary time-series analysis literature
- 📗 Highly comparative time-series analysis: the empirical structure of time series and their methods. J. Roy. Soc. Interface (2013).
We have used hctsa to:
- Distinguish meditators from non-meditators from 30s of resting-state EEG data.
- Identify neurophysiological signatures of cortical micro-architecture.
- Classify stars from NASA's Kepler Mission.
- Determine how striatal neuromodulation affect brain dynamics in thalamus and cortex.
- Uncover the dynamical structure of sleep EEG.
- Show how gradients of variation in time-series properties of BOLD dynamics vary with physiological variation and structural connectivity in the human neocortex.
- Distinguish targeted perturbations to mouse fMRI dynamics
- 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
(Let me know if I've missed any!)
hctsa has also been used to:
- Extract acoustic features from social vocal accommodation in adult marmoset monkeys. 📙 bioRxiv (2023).
- Detect anger from photoplethysmography (PPG) sensors 📗 Journal of NeuroEngineering and Rehabilitation (2023).
- Identify and distinguish marmoset vocalisations from audio, using Adaboost feature selection from hctsa features 📙 bioRxiv (2023).
- Track Drosophila in real time for high-throughput behavioral phenotyping. 📙 bioRxiv (2022).
- Discriminate zebra finch songs in different social contexts. 📗 PLoS Computational Biology (2021).
- Distinguish electromagnetic field exposure from zebrafish locomotion time series. 📗 Sensors (2020).
- Assess stress-induced changes in astrocyte calcium dynamics. 📗 Nature Communications (2020).
- Assess the stress controllability of neurons from their activity time series. 📗 Nature Neuroscience (2020).
- To capture a distinctive fingerprint of a individual's resting-state fMRI data. 📙 ResearchSquare (2023).
- As a partial source of features for building a cognitive assessment model from resting-state EEG 📙 ResearchSquare (2023).
- Identify methamphetamine users from EEG time series 📙 ResearchSquare (2023).
- Compute temporal profile similarity for individual fingerprinting from human fMRI data. 📗 Network Neuroscience (2023).
- Extract gradients from fMRI hctsa time-series features to understand the relationship between schizophrenia and nicotine dependence. 📗 Cerebral Cortex (2023).
- 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). 📙 SciTePress (2023).
- Characterize subnetworks of the frontoparietal control network from fMRI recordings. 📙 bioRxiv (2023).
- Distinguish motor-evoked potentials corresponding to multiple sclerosis. 📗 Front. Neuroinform. (2020).
- Find time-series properties of motor-evoked potentials that predict multiple sclerosis progression after two years. 📗 BMC Neurology (2020).
- Detect mild cognitive impairment using single-channel EEG to measure speech-evoked brain responses. 📗 IEEE Transactions on Neural Systems and Rehabilitation Engineering (2019).
- Predicting post cardiac arrest outcomes. 📗 Anaesthesia Critical Care & Pain Medicine (2022).
- Discover signatures of fatal neonatal illness from vital signs. 📗 npj Digital Medicine (2022).
- Prediction of post-cardiac arrest (CA) outcomes at discharge from physiological time series recorded on the first day of intensive care. 📗 Anaesthesia Critical Care & Pain Medicine (2021).
- Detect falls from wearable sensor data. 📗 Scientific Reports (2021).
- Detect falls from wearable sensor data. 📗 Biosensors (2021).
- Detect falls in elderly people from accelerometer data. 📗 IEEE International Conference on Information and Communication Technology for Sustainable Development (2021).
- Detect falls of elderly people using wearable sensors. 📗 IEEE Access (2021).
- Demonstrate that the suppression of essential tremor is due to a disruption of oscillations in the olivocerebellar loop. 📗 Nature Communications (2021).
- Classify heartbeats measured using single-lead ECG. 📗 IEEE 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (2019).
- Assess muscles for clinical rehabilitation. 📗 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2017).
- Select features for fetal heart rate analysis using genetic algorithms. 📗 Physiological Measurement (2014).
- Screen for COVID-19 using digital holographic microscopy. 📗 Biomedical Optics Express (2022).
- Detect COVID-19 from red blood cells using digital holographic microscopy. 📗 Optics Express (2022).
- Identify the biogeographic heterogeneity of mucus, lumen, and feces. 📗 PNAS (2021).
- Detect keyhole porosity formation during laser irradiation of Ti-6Al-4V substrates. 📗 Additive Manufacturing (2023).
- 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. 📗 Proceedings of the Asia Pacific Conference of the PHM Society (2023).
- Identifying keyhole pores in a laser powder-bed fusion process using acoustic and inline pyrometry time series. 📗 Journal of Materials Processing Technology (2022).
- Identify faults in a large-scale industrial process. PhD Thesis.
- Detect false data injection attacks into smart meters. 📗 IEEE Access (2021).
- Predict pending loss of power stability from generator response signals. 📗 IEEE Access (2021).
- Detect seeded bearing faults on a wind turbine subjected to non-stationary wind speed 📗 Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (2021).
- Recognize hand gestures. 📗 PLoS ONE (2020).
- Distinguish energy use behaviors from smart meter data. 📗 Energy and Buildings (2019).
- Non-intrusively monitor load for appliance detection and electrical power saving in buildings. 📗 Energy and Buildings (2019).
- Evaluate asphalt irregularity from smartphone sensors. 📗 International Symposium on Intelligent Data Analysis (2018).
- Detecting earthquakes from seismic recordings 📗 Geophysical Prospecting (2023).
- Find temporal patterns for reconstructing surface soil moisture time series 📗 Journal of Hydrology (2023).
- Predict earthquakes (in the following month) from seismic indicators in Bangladesh. 📗 IEEE Access (2021).
- Detect earthquakes in Groningen, The Netherlands. 📗 82nd EAGE Annual Conference & Exhibition Workshop Programme (2020).