Key result
Novel Resnet and self-attention method achieves ~0.95 F1-score for assessing single-channel ECG signal quality.
Why the study?
Enormous amounts of wearable ECG data burden medical professionals, and noise or motion perturbations can cause false alarms, necessitating automatic raw ECG signal quality assessment.
A novel deep learning method combining Resnet and self-attention achieves high accuracy (F1-score 0.954) in automatically assessing the quality of single-channel ECG signals.
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.
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Liu et al. (2023) studied ECG signal quality assessment. Resnet and self-attention based ECG signal quality assessment method was evaluated on F1-score for distinguishing between acceptable and unacceptable ECG segments. A new single-channel ECG signal quality assessment method combining Resnet and self-attention achieved an F1-score of 0.954 for distinguishing between acceptable and unacceptable ECG segments.
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