Ozal Yildirim1, Ulas Baran Baloglu2, Ru-San Tan3, Edward J Ciaccio4, U Rajendra Acharya5. 1. Department of Computer Engineering, Munzur University, Tunceli, Turkey. Electronic address: oyildirim@munzur.edu.tr. 2. Department of Computer Engineering, Munzur University, Tunceli, Turkey. 3. Department of Cardiology, National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore. 4. Department of Medicine - Division of Cardiology, Columbia University, USA. 5. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore School of Social Sciences, Singapore; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500 Subang Jaya, Malaysia.
Abstract
BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor have limited hardware capabilities. For improved diagnostic capacity, it would be helpful to detect arrhythmic signals automatically. In this study, a novel approach is presented as a candidate solution for these issues. METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network. RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed. CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.
BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor have limited hardware capabilities. For improved diagnostic capacity, it would be helpful to detect arrhythmic signals automatically. In this study, a novel approach is presented as a candidate solution for these issues. METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network. RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed. CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.
Authors: Edward J Ciaccio; Elaine Y Wan; Deepak S Saluja; U Rajendra Acharya; Nicholas S Peters; Hasan Garan Journal: Comput Methods Programs Biomed Date: 2019-06-15 Impact factor: 5.428
Authors: Nehemiah Musa; Abdulsalam Ya'u Gital; Nahla Aljojo; Haruna Chiroma; Kayode S Adewole; Hammed A Mojeed; Nasir Faruk; Abubakar Abdulkarim; Ifada Emmanuel; Yusuf Y Folawiyo; James A Ogunmodede; Abdukareem A Oloyede; Lukman A Olawoyin; Ismaeel A Sikiru; Ibrahim Katb Journal: J Ambient Intell Humaniz Comput Date: 2022-07-07