Abstract：Electrocardiogram (ECG) signal classification is significant research issue in the healthcare field. Signal data from ECG are classification imbalanced, and different classification of arrhythmias depend on long-term variation features, local variation features and their relative location of electrocardiogram. Most existing methods are not able to solve the classification imbalance problem well and consider the importance of specific waveforms. In this study, a hybrid neural network algorithm based on the location attention mechanisms was proposed for classifying ECG signals, referred to as DCLB algorithm. Firstly, the small-size classification samples were augmented adopting the deep convolutional generative adversarial networks (DCGAN) to solve the classification imbalance problem. Secondly, the local variation features and long-term variation features of ECG signals were extracted utilizing two-dimensional convolutional neural networks (2DCNN) and bi-directional long short-term memory network (BLSTM). Next, the location attention mechanisms (LAM) were nested behind each 2DCNN for enhancing the effects of key location features. Finally, the classification results were output using the fully connected neural networks. Experimental results on 30 584 samples of the MIT-BIH arrhythmia database showed that the proposed algorithm achieved the average accuracy of 98.79%, sensitivity of 94.21%, specificity of 98.98%, and positive predictive value of 93.70%. respectively. The results indicated that DCLB was able to extract effectively ECG signal features and suitable for the diagnosis of arrhythmia in the monitoring system.
龚玉晓, 高淑萍. 基于位置注意力机制的混合神经网络心电信号分类算法[J]. 中国生物医学工程学报, 2024, 43(3): 295-305.
Gong Yuxiao, Gao Shuping. A Hybrid Neural Network Electrocardiogram Signal Classification Algorithm Based on LocationAttention Mechanisms. Chinese Journal of Biomedical Engineering, 2024, 43(3): 295-305.
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