Affiliation:
1. Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract
Electrocardiogram (ECG) signals are among the significant physiological signals that indicate the essential properties of the human body. In recent years, the measurement of ECG signals has become more portable thanks to the increasing usage of wearable health testing technology. However, the enormous amount of signal data gathered over a long period of time does impose a heavy load on medical professionals. In addition, false alarms might occur due to the potential for the detected signal to become jumbled with noise and motion perturbations. Therefore, analyzing the quality of the measured raw ECG signal automatically is a valuable task. In this paper, we propose a new single-channel ECG signal quality assessment method that combines the Resnet network structure and the principle of self-attention to extract ECG signal features using the principle of similarity between individual QRS heartbeats within a time slice of ten seconds. In addition, an improved self-attention module is introduced into the deep neural network to learn the similarity between features. Finally, the network distinguishes between acceptable and unacceptable ECG segments. The model test results indicate that the F1-score can approach 0.954, which leads to a more accurate assessment of the ECG signal quality.
Funder
National Key R&D Program of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference53 articles.
1. (2022, October 08). Cardiovascular Diseases. Available online: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).
2. Mendis, S., Puska, P., Norrving, B., and World Health Organization (2011). Global Atlas on Cardiovascular Disease Prevention and Control, World Health Organization.
3. Kumar, P., and Sharma, V.K. (2021, January 4–6). Cardiac Signals Based Methods For Recognizing Heart Disease: A Review. Proceedings of the 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India.
4. Islam, M.R., Ahmad, S., Hirose, K., and Molla, M.K.I. (June, January 30). Data adaptive analysis of ECG signals for cardiovascular disease diagnosis. Proceedings of the 2010 IEEE International Symposium on Circuits and Systems, Paris, France.
5. Cardiac arrhythmia detection using deep learning;Isin;Procedia Comput. Sci.,2017
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