Heart rate variability in psychology: A review of HRV indices and an analysis tutorial
The use of heart rate variability (HRV) in research has been greatly popularized over the
past decades due to the ease and affordability of HRV collection, coupled with its clinical …
past decades due to the ease and affordability of HRV collection, coupled with its clinical …
Machine learning and decision support in critical care
Clinical data management systems typically provide caregiver teams with useful information,
derived from large, sometimes highly heterogeneous, data sources that are often changing …
derived from large, sometimes highly heterogeneous, data sources that are often changing …
[HTML][HTML] Application of Kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal CT images
Kidney stone disease is a serious public health concern that is getting worse with changes
in diet, obesity, medical conditions, certain supplements etc. A kidney stone also called a …
in diet, obesity, medical conditions, certain supplements etc. A kidney stone also called a …
GNN-SubNet: disease subnetwork detection with explainable graph neural networks
B Pfeifer, A Saranti, A Holzinger - Bioinformatics, 2022 - academic.oup.com
Motivation The tremendous success of graphical neural networks (GNNs) already had a
major impact on systems biology research. For example, GNNs are currently being used for …
major impact on systems biology research. For example, GNNs are currently being used for …
Epileptic seizure detection in EEG using mutual information-based best individual feature selection
Epilepsy is a group of neurological disorders that affect normal brain activities and human
behavior. Electroencephalogram based automatic epileptic seizure detection has significant …
behavior. Electroencephalogram based automatic epileptic seizure detection has significant …
Slope entropy: A new time series complexity estimator based on both symbolic patterns and amplitude information
D Cuesta-Frau - Entropy, 2019 - mdpi.com
The development of new measures and algorithms to quantify the entropy or related
concepts of a data series is a continuous effort that has brought many innovations in this …
concepts of a data series is a continuous effort that has brought many innovations in this …
A novel machine learning framework for automated detection of arrhythmias in ECG segments
Abstract Arrhythmias such as Atrial Fibrillation (A fib), Atrial Flutter (A fl), and Ventricular
Fibrillation (V fib) are early indicators of Stroke and Sudden Cardiac Death, which are …
Fibrillation (V fib) are early indicators of Stroke and Sudden Cardiac Death, which are …
[HTML][HTML] Minimising redundancy, maximising relevance: HRV feature selection for stress classification
Heart rate variability serves as a valuable indicator and biomarker for stress detection and
monitoring. Feature selection, which aims to identify relevant features from a large set of …
monitoring. Feature selection, which aims to identify relevant features from a large set of …
Determination of sample entropy and fuzzy measure entropy parameters for distinguishing congestive heart failure from normal sinus rhythm subjects
Entropy provides a valuable tool for quantifying the regularity of physiological time series
and provides important insights for understanding the underlying mechanisms of the …
and provides important insights for understanding the underlying mechanisms of the …
Approximate entropy profile: a novel approach to comprehend irregularity of short-term HRV signal
Kolmogorov–Sinai entropy-based irregularity measures such as approximate entropy
(ApEn), sample entropy and fuzzy entropy are widely used for short-term heart rate …
(ApEn), sample entropy and fuzzy entropy are widely used for short-term heart rate …