2019/06/18 by Chunyu Tan, Liming Zhang, Tan, Chunyu +6 · 1 citation
Engineering · Medicine · Neuroscience · #ECG Monitoring and Analysis #EEG and Brain-Computer Interfaces #FOS: Electrical engineering #Phonocardiography and Auscultation Techniques #Signal Processing (eess.SP) #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1906.07361
arxiv created 2019/06/18 · openalex publication_date 2019/06/18 · arxiv updated 2019/06/19 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
This paper proposes a novel approach for heartbeat classification from single-lead electrocardiogram (ECG) signals based on the novel adaptive Fourier decomposition (AFD). AFD is a recently developed signal processing tool that provides useful morphological features, referred to as AFD-derived instantaneous frequency (IF) features, that are different from those provided by traditional tools. A support vector machine (SVM) classifier is trained with the AFD-derived IF features, ECG landmark features, and RR interval features. To evaluate the performance of the trained classifier, the Association for the Advancement of Medical Instrumentation (AAMI) standard is applied to the publicly available benchmark databases, including MIT-BIH arrhythmia database and MIT-BIH supraventricular arrhythmia database, to classify heartbeats from single-lead ECG. The overall performance in terms of sensitivities and positive predictive values is comparable to the state-of-the-art automatic heartbeat classification algorithms based on two-leads ECG.