ECG Signal Processing and Human State Detection Based on Wearable Electrodes. Issue 3 (June 2021)

• Record Type: Journal Article
• Authors: Bohan, Cui; Yang, Hongliu
• Is Part Of: Journal of physics. Volume 1952:Issue 3(2021)
• Publication Date: 2021-06
• Subjects: ECG signal -- Wavelet Transform algorithm -- human body status monitoring -- wearable device; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1952/3/032055
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Detection and Recognition of a human's continuous states in real time plays an important role in wearable devices. We present an approach for heart rhythm recognition and human status detection through the analysis of ECG signal. The algorithm is comprised of four components, including noise removal, QRS-P-T wave detection, features extraction and human states classification. Discrete wavelet transform (DWT) is applied for random background noise removal. "Moving Integral – Changing Slope" method is used to determine the location of QRS complex waves and other characteristic waves of ECG signal. Time interval, location, amplitude, area of characteristic waves and coefficients of the transform as the features of each ECG segment are input into support vector machine (SVM) so that the machine can judge the states of the human body. Finally, we develop a single-lead ECG delineation wearable system to collect data from 30 users of different ages and human body states for experimental verification. Our evaluation shows that this method can accurately detect different states of the human body in real time. In the future, it can be implemented on wearable devices to assist real-time physiological status monitoring or customized health planning.