ECG Signals Deep Compressive Sensing Framework Based on Multiscale Feature Fusion and SE Block

Electrocardiogram (ECG) is nowadays an important technology to be applied in the clinical diagnosis for the detection of the heart disease. But the large storage and high-burden transmission of the ECG data is a challenge. Therefore, the compressive sensing (CS) is appropriate to deal with those signals for it can compress and sample the signal at the same time. In order to get rid of the constraints in the traditional CS methods, we propose a compressive sensing framework based on multiscale feature fusion and SE block. In the compression process we use sequential convolutional layers instead of the traditional compressive sensing using measurement matrix projection for ECG signals. In the reconstruction process, the multi-scale feature fusion method is first used to fuse multiple feature maps output from the convolution layer to better extract signal features. Subsequently, Squeeze-and-Excitation (SE) block is used to further enhance the feature representation. Finally, sequence modeling of the ECG signal is performed using LSTM to obtain the reconstructed signal. The results show that the proposed method performs well on various datasets and evaluation metrics, in the case of SR = 0.4, the PRD and SNR of the experiments on the MIT-BIH Arrhythmia database are 1.55% and 37.66dB, respectively. The PRD and SNR of the experiments on the Non-Invasive Fetal ECG Arrhythmia Database were 2.48% and 34.57dB, respectively, which were the lowest among all the comparison methods, indicating that the proposed method has good ECG signal processing capability.