Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method

Abnormal heart rhythms are one of the significant health concerns worldwide. The current state-of-the-art to recognize and classify abnormal heartbeats is manually performed by visual inspection by an expert practitioner. This is not just a tedious task; it is also error prone and, because it is per...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 19; no. 23; p. 5079
Main Authors:	Kandala, Rajesh N V P S, Dhuli, Ravindra, Pławiak, Paweł, Naik, Ganesh R., Moeinzadeh, Hossein, Gargiulo, Gaetano D., Gunnam, Suryanarayana
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 21.11.2019 MDPI
Subjects:	Algorithms Arrhythmias, Cardiac - diagnosis Arrhythmias, Cardiac - physiopathology Cardiac arrhythmia Classification Databases, Factual Decomposition Deep learning electrocardiogram signal Electrocardiography Electrocardiography - methods Field programmable gate arrays fpga Heart Rate - physiology Humans improved complete ensemble empirical mode decomposition inter-patient scheme Labeling Machine learning Methods Morphology Neural networks Nonlinear Dynamics nonlinear features Signal Processing, Computer-Assisted Support vector machines voting Wavelet transforms electrocardiogram signal FPGA improved complete ensemble empirical mode decomposition inter-patient scheme voting classification nonlinear features
ISSN:	1424-8220, 1424-8220
Online Access:	Get full text
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