Deep unfolding for multi-measurement vector convolutional sparse coding to denoise unobtrusive electrocardiography signals - Fotiadou, Eleni; Melaet, Raoul; Vullings, Rik (Frontiers in Signal Processing, 2022)
The use of wearable technology for monitoring a person’s health status is becoming increasingly more popular. Unfortunately, this technology typically suffers from low-quality measurement data, making the acquisition of, for instance, the heart rate based on electrocardiography (ECG) data from non-adhesive sensors challenging. Such sensors are prone to motion artifacts and hence the ECG measurements require signal processing to enhance their quality and enable detection of the heart rate. Over the last years, considerable progress has been made in the use of deep neural networks for many signal processing challenges. Yet, for healthcare applications their success is limited because the required large datasets to train these networks are typically not available. In this paper we propose a method to embed prior knowledge about the measurement data and problem statement in the network architecture to make it more data efficient. Our proposed method aims to enhance the quality of ECG signals by describing ECG signals from the perspective of a multi-measurement vector convolutional sparse coding model and use a deep unfolded neural network architecture to learn the model parameters. The sparse coding problem was solved using the Alternation Direction Method of Multipliers. Our method was evaluated by denoising ECG signals, that were corrupted by adding noise to clean ECG signals, and subsequently detecting the heart beats from the denoised data and compare these to the heartbeats and derived heartrate variability features detected in the clean ECG signals. This evaluation demonstrated an improved in the signal-to-noise ratio (SNR) improvement ranging from 17 to 27 dB and an improvement in heart rate detection (i.e. F1 score) ranging between 0 and 50%, where the range depends on the SNR of the input signals. The performance of the method was compared to that of a denoising encoder-decoder neural network and a wavelet-based denoising method, showing equivalent and better performance, respectively.