Noise Cleaning of ECG on Edge Device Using Convolutional Sparse Contractive Autoencoder

Single-lead Electrocardiogram (ECG) can be easily measured by a commercial smartwatch or a dedicated wearable device. The waveforms are often susceptible to background noise and motion artifacts introducing errors in disease interpretation. An effective yet light-weight de-noising of ECG is an open area of research. In this paper, we propose a novel convolutional autoencoder structure considering a number of regularization terms like sparsity constraint, contractive regularization and L2 norm for ECG de-noising. The deep learning model is duly optimized to efficiently run on low-power edge devices. The proposed approach is evaluated on a simulated and a real-world single-lead ECG database recorded from normal subjects as well as patients having Atrial Fibrillation (AF) and other kinds of abnormal heart rhythms. A thorough comparison is performed with a number of related signal processing and deep learning based prior approaches. Experimental results show that the proposed autoencoder yields the least Root Mean Square Error (RMSE) in reconstruction of clean signals from input ECG corrupted due to addition of noise. Our approach is also able to preserve the relevant morphological properties in the reconstructed ECG data for successful detection of AF and other abnormal rhythms. The optimized model is deployed on a low-power single-board computer for real-time noise cleaning.