期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 25, 期 6, 页码 1904-1914出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2020.3035191
关键词
Electrocardiography; Feature extraction; Detection algorithms; Bioinformatics; Heart; Quantization (signal); Modulation; Electrocardiogram (ECG); delta-sigma modulator; random forest (RF); ECG classification
类别
资金
- Ministry of Science and Technology of Taiwan [MOST 109-2221-E002-128]
This paper presents a resource-saving system for extracting important features of ECG signals and real-time classifiers for arrhythmia classification. The proposed method achieves high performance in classifying different types of arrhythmias, with advantages of reliability, efficiency, low complexity, and low memory usage. It can be applied to practical ECG applications effectively.
This paper presents a resource-saving system to extract a few important features of electrocardiogram (ECG) signals. In addition, real-time classifiers are proposed as well to classify different types of arrhythmias via these features. The proposed feature extraction system is based on two delta-sigma modulators adopting 250 Hz sampling rate and three wave detection algorithms to analyze outputs of the modulators. It extracts essential details of each heartbeat, and the details are encoded into 68 bits data that is only 1.48% of the other comparable methods. To evaluate our classification, we use a novel patient-specific training protocol in conjunction with the MIT-BIH database and the recommendation of the AAMI to train the classifiers. The classifiers are random forests that are designed to recognize two major types of arrhythmias. They are supraventricular ectopic beats (SVEB) and ventricular ectopic beats (VEB). The performance of the arrhythmia classification reaches to the F1 scores of 81.05% for SVEB and 97.07% for VEB, which are also comparable to the state-of-the-art methods. The method provides a reliable and accurate approach to analyze ECG signals. Additionally, it also possesses time-efficient, low-complexity, and low-memory-usage advantages. Benefiting from these advantages, the method can be applied to practical ECG applications, especially wearable healthcare devices and implanted medical devices, for wave detection and arrhythmia classification.
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