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http://dx.doi.org/10.9718/JBER.2022.43.6.424

Acquisition and Classification of ECG Parameters with Multiple Deep Neural Networks  

Ji Woon, Kim (Interdisciplinary Program in Biohealth-Machinery Convergence Engineering, Kangwon National University)
Sung Min, Park (Department of Thoracic & Cardiovascular Surgery, School of Medicine, Kangwon National University)
Seong Wook, Choi (Interdisciplinary Program in Biohealth-Machinery Convergence Engineering, Kangwon National University)
Publication Information
Journal of Biomedical Engineering Research / v.43, no.6, 2022 , pp. 424-433 More about this Journal
Abstract
As the proportion of non-contact telemedicine increases and the number of electrocardiogram (ECG) data measured using portable ECG monitors increases, the demand for automatic algorithms that can precisely analyze vast amounts of ECG is increasing. Since the P, QRS, and T waves of the ECG have different shapes depending on the location of electrodes or individual characteristics and often have similar frequency components or amplitudes, it is difficult to distinguish P, QRS and T waves and measure each parameter. In order to measure the widths, intervals and areas of P, QRS, and T waves, a new algorithm that recognizes the start and end points of each wave and automatically measures the time differences and amplitudes between each point is required. In this study, the start and end points of the P, QRS, and T waves were measured using six Deep Neural Networks (DNN) that recognize the start and end points of each wave. Then, by synthesizing the results of all DNNs, 12 parameters for ECG characteristics for each heartbeat were obtained. In the ECG waveform of 10 subjects provided by Physionet, 12 parameters were measured for each of 660 heartbeats, and the 12 parameters measured for each heartbeat well represented the characteristics of the ECG, so it was possible to distinguish them from other subjects' parameters. When the ECG data of 10 subjects were combined into one file and analyzed with the suggested algorithm, 10 types of ECG waveform were observed, and two types of ECG waveform were simultaneously observed in 5 subjects, however, it was not observed that one person had more than two types.
Keywords
Electrocardiogram; Multiple deep neural network (mDNN); Tompkins method; Start/End point; ECG parameters;
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Times Cited By KSCI : 3  (Citation Analysis)
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