Convolutional block attention autoencoder for denoising electrocardiograms

Abstract

Electrocardiograms are commonly used to detect cardiovascular diseases, so it is important that they are of high quality. However, various sources of noise, such as baseline wander, muscle artifact, and electrode motion can obfuscate the signal of the heart recorded by different monitors. In this work, we propose a novel algorithm, convolutional denoising autoencoder with block attention module (CDAE-BAM), for removing noise from electrocardiograms by leveraging attention in a convolutional denoising autoencoder. We propose an attention block including both spatial and channel attention. Spatial attention captures the location of relevant features within channels in a signal, and channel attention captures the most relevant channels in a signal. We validate our proposed algorithm’s performance in removing noise from the MIT-BIH Noise Stress Test Database from electrocardiogram signals in the QT Database, the Computing in Cardiology Challenge 2017 Database, and the Medical Information Mart for Intensive Care Database. We show that this method outperforms eight other state-of-the-art methods with respect to sum of squared distances, mean absolute distance, and cosine similarity.