Noise Reduction in Photoplethysmography Signals using a Convolutional Denoising Autoencoder with Unconventional Training Scheme

• Published in: IEEE transactions on biomedical engineering Vol. 71; no. 2; pp. 1 - 11
• Main Authors: Mohagheghian, Fahimeh, Han, Dong, Ghetia, Om, Peitzsch, Andrew, Nishita, Nishat, Nejad, Mahdi Pirayesh Shirazi, Ding, Eric Y., Noorishirazi, Kamran, Hamel, Alexander, Otabil, Edith Mensah, DiMezza, Danielle, Dickson, Emily L., Tran, Khanh-Van, McManus, David D., Chon, Ki H.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive filters; Algorithms; Arrhythmia detection; Atrial fibrillation; Atrial Fibrillation - diagnosis; Autoencoder; Data models; Datasets; Denoising; Heart beat; Heart rate; Heart Rate - physiology; Humans; Image segmentation; Monitoring, Physiologic; Motion; Noise reduction; Photoplethysmography; Photoplethysmography - methods; Root-mean-square errors; Segments; Signal Processing, Computer-Assisted; Training; Wearable Health Monitoring Systems; Wearable technology
• DOI: 10.1109/TBME.2023.3307400
• ISSN: 0018-9294; 1558-2531

Objective : We propose an efficient approach based on a convolutional denoising autoencoder (CDA) network to reduce motion and noise artifacts (MNA) from corrupted atrial fibrillation (AF) and non-AF photoplethysmography (PPG) data segments so that an accurate PPG-signal-derived heart rate can be obtained. Our method's main innovation is the optimization of the CDA performance for both rhythms using more AF than non-AF data for training the AF-specific CDA model and vice versa for the non-AF CDA network. Methods : To evaluate this unconventional training scheme, our proposed network was trained and tested on 25-sec PPG data segments from 48 subjects from two different databases-the Pulsewatch dataset and Stanford University's publicly available PPG dataset. In total, our dataset contains 10,773 data segments: 7,001 segments for training and 3,772 independent segments from out-of-sample subjects for testing. Results : Using real-life corrupted PPG segments, our approach significantly reduced the average heart rate root mean square error (RMSE) of the reconstructed PPG segments by 45.74% and 23% compared to the corrupted non-AF and AF data, respectively. Further, our approach exhibited lower RMSE, and higher sensitivity and PPV for detected peaks compared to the reconstructed data produced by the alternative methods. Conclusion : These results show the promise of our approach as a reliable denoising method, which should be used prior to AF detection algorithms for an accurate cardiac health monitoring involving wearable devices. Significance : PPG signals collected from wearables are vulnerable to MNA, which limits their use as a reliable measurement, particularly in uncontrolled real-life environments.