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Cascade Classification with Adaptive Feature Extraction for Arrhythmia Detection

Title
Cascade Classification with Adaptive Feature Extraction for Arrhythmia Detection
Author
강경태
Keywords
ECG; Heartbeat classification; Heartbeat morphology features; Cascaded classifiers; Adaptive feature extraction; HEARTBEAT CLASSIFICATION; NEURAL-NETWORK; AUTOMATIC CLASSIFICATION; ECG SIGNALS; DATABASE; SYSTEM; RECOGNITION; SMARTPHONE
Issue Date
2017-01
Publisher
SPRINGER
Citation
JOURNAL OF MEDICAL SYSTEMS, v. 41, No. 1, Article no. 11
Abstract
Detecting arrhythmia from ECG data is now feasible on mobile devices, but in this environment it is necessary to trade computational efficiency against accuracy. We propose an adaptive strategy for feature extraction that only considers normalized beat morphology features when running in a resource-constrained environment; but in a high-performance environment it takes account of a wider range of ECG features. This process is augmented by a cascaded random forest classifier. Experiments on data from the MIT-BIH Arrhythmia Database showed classification accuracies from 96.59% to 98.51%, which are comparable to state-of-the art methods.
URI
https://link.springer.com/article/10.1007/s10916-016-0660-9http://repository.hanyang.ac.kr/handle/20.500.11754/71651
ISSN
0148-5598; 1573-689X
DOI
10.1007/s10916-016-0660-9
Appears in Collections:
COLLEGE OF COMPUTING[E] > COMPUTER SCIENCE(소프트웨어학부) > Articles
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