Using the Redundant Convolutional Encoder-Decoder to Denoise QRS Complexes in ECG Signals Recorded with an Armband Wearable Device

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 16; p. 4611
• Main Authors: Reljin, Natasa, Lazaro, Jesus, Hossain, Md Billal, Noh, Yeon Sik, Cho, Chae Ho, Chon, Ki H
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.08.2020; MDPI
• Subjects: Algorithms; Arrhythmia; Cardiac arrhythmia; denoising algorithm; electrocardiogram (ECG); Electrocardiography; Electrodes; Heart; Humans; Male; Measuring instruments; Monitoring, Physiologic; motion artifacts; Noise; Noise levels; Noise reduction; redundant convolutional encoder–decoder (R-CED); Signal processing; Signal Processing, Computer-Assisted; Signal quality; Signal to noise ratio; signal-to-noise ratio (SNR); Skin; Wavelet transforms; wearable devices; Wearable Electronic Devices; Wearable technology; electrocardiogram (ECG); redundant convolutional encoder–decoder (R-CED); wearable devices; ratio of power; signal-to-noise ratio (SNR); cross-correlation; motion artifacts; denoising algorithm
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20164611

Long-term electrocardiogram (ECG) recordings while performing normal daily routines are often corrupted with motion artifacts, which in turn, can result in the incorrect calculation of heart rates. Heart rates are important clinical information, as they can be used for analysis of heart-rate variability and detection of cardiac arrhythmias. In this study, we present an algorithm for denoising ECG signals acquired with a wearable armband device. The armband was worn on the upper left arm by one male participant, and we simultaneously recorded three ECG channels for 24 h. We extracted 10-s sequences from armband recordings corrupted with added noise and motion artifacts. Denoising was performed using the redundant convolutional encoder-decoder (R-CED), a fully convolutional network. We measured the performance by detecting R-peaks in clean, noisy, and denoised sequences and by calculating signal quality indices: signal-to-noise ratio (SNR), ratio of power, and cross-correlation with respect to the clean sequences. The percent of correctly detected R-peaks in denoised sequences was higher than in sequences corrupted with either added noise (70-100% vs. 34-97%) or motion artifacts (91.86% vs. 61.16%). There was notable improvement in SNR values after denoising for signals with noise added (7-19 dB), and when sequences were corrupted with motion artifacts (0.39 dB). The ratio of power for noisy sequences was significantly lower when compared to both clean and denoised sequences. Similarly, cross-correlation between noisy and clean sequences was significantly lower than between denoised and clean sequences. Moreover, we tested our denoising algorithm on 60-s sequences extracted from recordings from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and obtained improvement in SNR values of 7.08 ± 0.25 dB (mean ± standard deviation (sd)). These results from a diverse set of data suggest that the proposed denoising algorithm improves the quality of the signal and can potentially be applied to most ECG measurement devices.