https://scholars.lib.ntu.edu.tw/handle/123456789/580660
標題: | An Efficient ECG Classification System Using Resource-Saving Architecture and Random Forest | 作者: | CHIEH-HSIUNG KUAN Kung B.-H Hu P.-Y Huang C.-C Lee C.-C Yao C.-Y CHIEH-HSIUNG KUAN |
關鍵字: | Classification (of information); Computer architecture; Decision trees; Diseases; Electrocardiography; Feature extraction; Modulators; Random forests; Signal detection; Arrhythmia classification; Delta sigma modulator; Ecg classifications; Electrocardiogram signal; Implanted medical devices; Real-time classifiers; State-of-the-art methods; Ventricular ectopic beats; Biomedical signal processing | 公開日期: | 2021 | 卷: | 25 | 期: | 6 | 起(迄)頁: | 1904-1914 | 來源出版物: | IEEE Journal of Biomedical and Health Informatics | 摘要: | 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. ? 2013 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098778501&doi=10.1109%2fJBHI.2020.3035191&partnerID=40&md5=9fd89cdef1f57e0634ac6e7b7967addc https://scholars.lib.ntu.edu.tw/handle/123456789/580660 |
ISSN: | 21682194 | DOI: | 10.1109/JBHI.2020.3035191 | SDG/關鍵字: | Classification (of information); Computer architecture; Decision trees; Diseases; Electrocardiography; Feature extraction; Modulators; Random forests; Signal detection; Arrhythmia classification; Delta sigma modulator; Ecg classifications; Electrocardiogr |
顯示於: | 電機工程學系 |
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