Review and classification of variability analysis techniques with clinical applications

A Bravi, A Longtin, AJE Seely - Biomedical engineering online, 2011 - Springer
Abstract Analysis of patterns of variation of time-series, termed variability analysis,
represents a rapidly evolving discipline with increasing applications in different fields of …

Efficient classification of ventricular arrhythmias using feature selection and C4. 5 classifier

M Mohanty, S Sahoo, P Biswal, S Sabut - Biomedical Signal Processing …, 2018 - Elsevier
The occurrence of sudden cardiac arrest (SCA) leads to a massive death across the world.
Hence the early prediction of ventricular tachycardia (VT) and ventricular fibrillation (VF) …

Prediction and classification of ventricular arrhythmia based on phase-space reconstruction and fuzzy c-means clustering

H Chen, S Das, JM Morgan, K Maharatna - Computers in Biology and …, 2022 - Elsevier
Background and objective Prediction and classification of Ventricular Arrhythmias (VA) may
allow clinicians sufficient time to intervene for stopping its escalation to Sudden Cardiac …

A real-time QRS detection method based on phase portraits and box-scoring calculation

Z Hou, Y Dong, J Xiang, X Li, B Yang - IEEE Sensors Journal, 2018 - ieeexplore.ieee.org
In order to detect the QRS complexes locally in the microcontroller-based embedded
system, a novel algorithm with lower computation burden is proposed by phase space …

Detection of ventricular tachycardia and fibrillation using adaptive variational mode decomposition and boosted-CART classifier

Y Xu, D Wang, W Zhang, P Ping, L Feng - Biomedical Signal Processing …, 2018 - Elsevier
Rapid ventricular tachycardia (VT) and ventricular fibrillation (VF) are serious life-threatening
ventricular arrhythmias. Correct detection of VT/VF is crucial for the rescue of cardiac arrest …

Ventricular fibrillation and tachycardia detection using features derived from topological data analysis

A Mjahad, JV Frances-Villora, M Bataller-Mompean… - Applied Sciences, 2022 - mdpi.com
Featured Application Automated External Defibrillation (AED) and Implantable Cardioverter
Defibrillators (ICD) require accurate algorithms to detect arrhythmias and discriminate …

Network representations of attractors for change point detection

E Tan, SD Algar, D Corrêa, T Stemler… - Communications …, 2023 - nature.com
A common approach to monitoring the status of physical and biological systems is through
the regular measurement of various system parameters. Changes in a system's underlying …

Automated classification of hypertension and coronary artery disease patients by PNN, KNN, and SVM classifiers using HRV analysis

MG Poddar, AC Birajdar, J Virmani - … learning in bio-signal analysis and …, 2019 - Elsevier
The hypertension (HTN) and coronary artery disease (CAD) are the major cardiovascular
diseases existing globally. In the present work, the heart rate variability (HRV) of normal …

Individual identification based on chaotic electrocardiogram signals during muscular exercise

SL Lin, CK Chen, CL Lin, WC Yang… - IET Biometrics, 2014 - Wiley Online Library
An electrocardiogram (ECG) records changes in the electric potential of cardiac cells using a
noninvasive method. Previous studies have shown that each person's cardiac signal …

Efficient extraction of deep image features using a convolutional neural network (CNN) for detecting ventricular fibrillation and tachycardia

A Mjahad, M Saban, H Azarmdel, A Rosado-Muñoz - Journal of Imaging, 2023 - mdpi.com
To safely select the proper therapy for ventricular fibrillation (VF), it is essential to distinguish
it correctly from ventricular tachycardia (VT) and other rhythms. Provided that the required …