An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention

Author:

Liu Yuying12,Zhang Hao1,Zhao Kun1,Liu Haiyang1,Long Fei1,Chen Liping1,Yang Yaguang1

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

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference53 articles.

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