Publications using hctsa

This page lists scientific research publications that have used hctsa.

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!

Our Research 📕

Methods Papers

The following publications for details of how the highly-comparative approach to time-series analysis has developed since our initial publication in 2013. We:

Reduced the hctsa feature library down to a reduced set of 22 efficiently coded features: catch22.

📗 Data Mining and Knowledge Discovery 33, 1821 (2019).

💻 catch22 Code.

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).

📗 Cell Systems 5, 527 (2017).

💻 Code (fly).

💻 Code (worm).

Introduced the feature-based time-series analysis methodology.

📗 Feature Engineering for Machine Learning and Data Analytics, CRC Press (2018).

📙 Preprint.

Showed that the behaviour of thousands of time-series methods on thousands of different time series can be used to organise the interdisciplinary time-series analysis literature.

📗 J. Roy. Soc. Interface (2013).

Applications Papers

We have used hctsa to:

Find dynamical signatures of psychiatric disorders from resting-state fMRI data.

📙 bioRxiv (2023).

Predict individual response to rTMS depression treatment from EEG data.

📙 medRxiv (2023).

Distinguish meditators from non-meditators from 30s of resting-state EEG data.

📗 Neural Networks (2023).

Identify neurophysiological signatures of cortical micro-architecture.

📗 Nature Comms. (2023).

Classify stars from NASA's Kepler Mission.

📗 Monthly Notices of the Royal Astronomical Society (2022).

Determine how striatal neuromodulation affects brain dynamics in thalamus and cortex.

📗 eLife (2023).

Uncover the dynamical structure of sleep EEG.

📗 Sleep Medicine (2022).

Show how gradients of variation in time-series properties of BOLD dynamics vary with physiological variation and structural connectivity in the human neocortex.

📗 eLife (2020).

Distinguish targeted perturbations to mouse fMRI dynamics.

📗 Cerebral Cortex (2020).

💻 Code.

as well as:

Connect structural brain connectivity to fMRI dynamics (mouse).
- 📗 Chaos (2017).
Connect structural brain connectivity to fMRI dynamics (human).
- 📗 Network Neuroscience (2020).
Distinguish time-series patterns for data-mining applications.
- 📗 IEEE Trans. Knowl. Data Eng. (2014).
Classify babies with low blood pH from fetal heart rate time series.
- 📗 34th Ann. Int. Conf. IEEE EMBC (2012).

Others' Research 📕

🧬 Biology

Extract acoustic features from social vocal accommodation in adult marmoset monkeys.

📙 bioRxiv (2023).

Track Drosophila in real time for high-throughput behavioural phenotyping.

📗 eLife (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.

📗 J. Roy. Soc. Interface (2023).

Discriminate zebra finch songs in different social contexts.

📗 PLoS Computational Biology (2021).

Distinguish electromagnetic field exposure from zebrafish locomotion time series.

📗 Sensors (2020).

🧫 Cellular Neuroscience

Confirm the role of µORs in VTA and NAc in acute fentanyl-induced behaviour (positive reinforcement).

📗 Chaudun et al., Nature (2024).

Assess stress-induced changes in astrocyte calcium dynamics.

📗 Nature Comms. (2020).

Assess the stress controllability of neurons from their activity time series.

📗 Nature Neuroscience (2020).

🧠 Neuroimaging

Here are some highlights:

Understand changes in fMRI brain dynamics in patients with epilepsy.

📗 Communications Biology (2024).

Extract EEG markers of cognitive decline.

📗 npj Aging (2024).

Detect EEG markers of seizure disorders.

📗 Brain Communications (2023).

Capture a distinctive fingerprint of an individual's resting-state fMRI data.

📙 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).

Characterise subnetworks of the frontoparietal control network from fMRI recordings.

📙 bioRxiv (2023).

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).

In addition to:

Predict age from resting-state MEG from individual brain regions.
- �al., PNAS (2025).
Estimate brain age in children from EEG.
- 📗 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (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 hctsafeatures).
- 📙 SciTePress (2023).
Distinguish motor-evoked potentials corresponding to multiple sclerosis.
- 📗 Frontiers in Neuroinformatics (2020).

🔬 Medicine—General

Here are some highlights:

Differentiate tremor disorders using massive feature extraction, outperforming the best traditional tremor statistic.

📙 MedRxiv (2024).

Identify physiological features predictive of respiratory outcomes in extremely pre-term infants from bedside monitor data.

📙 MedRxiv (2024)

Discover signatures of fatal neonatal illness from vital signs.

📗 npj Digital Medicine (2022).

Detect falls in elderly people from accelerometer data.

📗 IEEE International Conference on Information and Communication Technology for Sustainable Development (2021).

Prediction of post-cardiac arrest outcomes at discharge from physiological time series recorded on the first day of intensive care.

📗 Anaesthesia Critical Care & Pain Medicine (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 Comms. (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).

in addition to:

Predict MS disability progression using time-series features of evoked potential signals
- 📗 Fonteyn et al. International Conference on Machine Learning and Applications (ICMLA). (2025).
Identify sepsis in very low birth weight (<1.5kg) infants from heart rate signals, identifying heart rate characteristics of reduced variability and transient decelerations.
- 📙 MedRxiv (2024)
Identify novel heart-rate variability metrics, including RobustSD, to create a parsimonious model for cerebral palsy prediction in preterm neonatal intensive care unit patients.
- 📗 Pediatric Research (2023).
Predicting post cardiac arrest outcomes.
- 📗 Anaesthesia Critical Care & Pain Medicine (2022).
Detect falls from wearable sensor data.
- 📗 Scientific Reports (2021).
Detect falls from wearable sensor data.
- 📗 Biosensors (2021).
Select features for fetal heart rate analysis using genetic algorithms.
- 📗 Physiological Measurement (2014).

🦠 Medicine—Pathology

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).

🏗 Engineering

Here are some highlights:

Detect keyhole porosity formation during laser irradiation of Ti-6Al-4V substrates.

📗 Additive Manufacturing (2023).

Identify keyhole pores in a laser powder-bed fusion process using acoustic and inline pyrometry time series.

📗 Journal of Materials Processing Technology (2022).

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).

Recognise hand gestures.

📗 PLoS ONE (2020).

Distinguish energy use behaviours 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).

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.
- 📗 Proceedings of the Asia Pacific Conference of the PHM Society (2023).
Identify faults in a large-scale industrial process.
- PhD Thesis.