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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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 19; H. 23; S. 5079
Hauptverfasser:	Kandala, Rajesh N V P S, Dhuli, Ravindra, Pławiak, Paweł, Naik, Ganesh R., Moeinzadeh, Hossein, Gargiulo, Gaetano D., Gunnam, Suryanarayana
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 21.11.2019 MDPI
Schlagworte:	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-Zugang:	Volltext
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Zusammenfassung:	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 performed, post-recordings may add unnecessary delay to the care. The real key to the fight to cardiac diseases is real-time detection that triggers prompt action. The biggest hurdle to real-time detection is represented by the rare occurrences of abnormal heartbeats and even more are some rare typologies that are not fully represented in signal datasets; the latter is what makes it difficult for doctors and algorithms to recognize them. This work presents an automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies. Although the algorithm is designed and tested on a computer, it is intended ultimately to run on a portable i.e., field-programmable gate array (FPGA) devices. Our algorithm tested on Massachusetts Institute of Technology- Beth Israel Hospital(MIT-BIH) database as per Association for the Advancement of Medical Instrumentation(AAMI) recommendations. The simulation results show the superiority of the proposed method, especially in predicting minority groups: the fusion and unknown classes with 90.4% and 100%.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1424-8220 1424-8220
DOI:	10.3390/s19235079