Reduced the hctsa feature library down to a reduced set of 22 efficiently coded features: catch22.
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!
The following publications for details of how the highly-comparative approach to time-series analysis has developed since our initial publication in 2013. We:
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.
Here are some highlights:
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 hctsafeatures).
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.
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.
Extract EEG markers of cognitive decline.
.
.
.
📙
.
📗 .
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
📗
.
.
.
.
.
.
.
.
.
.
📗
.
.
.
.
.
.
.
.
.
.
