A novel attentional deep neural network-based assessment method for ECG quality. (January 2023) - Jin, Yanrui; Li, Zhiyuan; Qin, Chengjin; Liu, Jinlei; Liu, Yunqing; Zhao, Liqun; Liu, Chengliang
Highlights: This paper presents a novel dual attentional convolutional long short-term memory neural network to reject noise for ECG quality assessment. Convolution layers and bidirectional long-short memory layer are integrated to extract features from dual-scale inputs and acquire good classification performance. Channel-based and time-based attention mechanisms are presented to visually show the attention of the model to different leads and different periods on the multi-leads ECG signals. To our knowledge, it is the first effort for introducing attention mechanism in ECG quality assessment. The experimental results show that the proposed method achieves 76.47% of specificity, 97.59% of sensitivity, and 94.0% of accuracy, especially improving 3.35% accuracy on average and 4.27% sensitivity on average. Abstract: ECG quality assessment is of great significance to reduce false alarms in automatic arrhythmia and other cardiovascular diseases diagnoses and reduce the workload of clinicians. Recently, developing an automatic noise rejection algorithm attracts much attention. Nowadays, many researchers have applied deep learning (DL) algorithms into evaluating ECG quality. However, traditional DL approaches improve model accuracy but cannot show the concerned area of ECG signals during detection process. Hence, this paper presents dual attentional convolutional long short-term memory neural network (DAC-LSTM) to evaluate ECG quality. Firstly, convolutional and bidirectional long short-term memory layers are utilized for acquiring numerous deep features from ECG recordings. And then, for enhancing model interpretability, dual-layer attention mechanisms, including channel-based attention mechanism and time-based attention mechanism, are built to visually show the attention of the model to different leads and different periods on the multi-leads ECG signals. Finally, compared with baseline models and the existing methods, DAC-LSTM achieves 76.47% of specificity, 97.59% of sensitivity, and 94.0% of accuracy, especially improving 3.35% accuracy on average and 4.27% sensitivity on average on the commonly used ECG dataset. Generally, DAC-LSTM achieves competitive and interpretable performance and has the potential for practical ECG quality assessment.