HRV based feature selection for congestive heart failure and normal sinus rhythm for meticulous presaging of heart disease using machine learning
Open Access
- 1 December 2022
- journal article
- Published by Elsevier BV in Measurement: Sensors
Abstract
No abstract availableThis publication has 11 references indexed in Scilit:
- A Multidimensional Feature Extraction and Selection Method for ECG Arrhythmias ClassificationIEEE Sensors Journal, 2020
- Heart Rate Variability in Patients with Hypertension: the Effect of Metabolic Syndrome and Antihypertensive TreatmentCardiovascular Therapeutics, 2020
- Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image RepresentationRemote Sensing, 2020
- Application of a convolutional neural network for predicting the occurrence of ventricular tachyarrhythmia using heart rate variability featuresScientific Reports, 2020
- Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG SignalSensors, 2020
- Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning TechniquesBioMed Research International, 2020
- Machine Learning Analysis of Heart Rate Variability for the Detection of Seizures in Comatose Cardiac Arrest SurvivorsIEEE Access, 2020
- Principal Component Analysis based on data characteristics for dimensionality reduction of ECG recordings in arrhythmia classificationOpen Physics, 2019
- Heart Rate Variability (HRV) Based Feature Extraction for Congestive Heart FailureInternational Journal of Computer and Electrical Engineering, 2016
- DETECTION OF HUMAN STRESS USING SHORT-TERM ECG AND HRV SIGNALSJournal of Mechanics in Medicine and Biology, 2013