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

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 ob...