NGA-Net: an ECG waveform segmentation algorithm based on semisupervised learning

Targeting the challenge where the substantial labeling expense of ECG data contributes to the present dearth of labeled ECG datasets and the subpar segmentation precision of contemporary models, this paper proposes an ECG segmentation model NGA-Net,the model is based on RRU-Net, with the addition of the ASPNL module and the improved Ghost module, in which the improved Ghost module is designed to generate an increased quantity of feature maps using a reduced parameter set, thereby boosting computational efficiency; The ASPNL module can capture ECG signal features from multiple scales to enhance the efficiency of feature extraction. Experimental evidence indicates that the ECG segmentation model, NGA-Net, introduced in this research, exhibits superior performance in comparison to other methodologies when tested on the publicly available LUDB dataset, which demonstrates the effectiveness of NGANet.In this research, we adopt a semi-supervised learning strategy for training the NGA-Net in scenarios with small sample sizes, leveraging data augmentation and consistency training methodologies. The experimental findings corroborate the effectiveness of semi-supervised learning in augmenting the performance of deep learning models.