Multi-Task Learning-Based Quality Assessment and Denoising of Electrocardiogram Signals

In recent years, there has been a surge in applying deep learning methods for signal quality assessment (SQA) and denoising of ECG signals. However, solely focusing on denoising can lead to situations where models trained on severely contaminated signals perform poorly on higher-quality signals. Likewise, carrying out SQA in isolation may be constrained by the number of training data and susceptible to label noise. In this study, we leverage the correlation between denoising and SQA and propose a multi-task learning network (MTL-NET) that addresses both tasks in parallel to mitigate the challenges. MTL-NET employs a stacked bidirectional LSTM network, combined with an attention module, as a shared backbone to extract common features for both SQA and denoising tasks. Weighted loss functions are utilized to balance the performance of these two tasks. Within MTL-NET, we classify ECG signal quality into three categories, and compare MTL-NET’s performance with baseline methods dedicated solely to denoising or three-class SQA. The results demonstrate that MTL-NET outperforms other denoising methods across all metrics obviously, including the corresponding single-task denoising model. Specifically, MTL-NET significantly enhances the denoising performance of the "intermediate" quality category and the SQA performance in identifying ‘unacceptable’ signal segments. It provides a novel perspective on ECG signal preprocessing by implementing a balance rule through loss functions for both tasks within a model. With 0.28M parameters and 27.35M FLOPs, the MTL-NET is not only efficient but also exhibits excellent generalization performance on an independent dataset, making it suitable for deployment in resource-constrained environments.