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/s10916-016-0660-9https://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](소프트웨어융합대학) > COMPUTER SCIENCE(소프트웨어학부) > Articles
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