PPG2ECGps: An End-to-End Subject-Specific Deep Neural Network Model for Electrocardiogram Reconstruction from Photoplethysmography Signals without Pulse Arrival Time Adjustments

• Published in: Bioengineering (Basel) Vol. 10; no. 6; p. 630
• Main Authors: Tang, Qunfeng, Chen, Zhencheng, Ward, Rabab, Menon, Carlo, Elgendi, Mohamed
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.05.2023; MDPI
• Subjects: Accessibility; AI in healthcare; Alignment; Artificial neural networks; Bioengineering; Blood pressure; Cardiovascular system; Correlation coefficients; Datasets; digital health; EKG; Electrocardiogram; electrocardiogram construction; Electrocardiography; Heart; Machine learning; Methods; Neural networks; photoplethysmography; Physiology; remote monitoring; Signal reconstruction; Wearable technology; United States; electrocardiogram construction; remote monitoring; digital health; photoplethysmography; AI in healthcare
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering10060630

Electrocardiograms (ECGs) provide crucial information for evaluating a patient’s cardiovascular health; however, they are not always easily accessible. Photoplethysmography (PPG), a technology commonly used in wearable devices such as smartwatches, has shown promise for constructing ECGs. Several methods have been proposed for ECG reconstruction using PPG signals, but some require signal alignment during the training phase, which is not feasible in real-life settings where ECG signals are not collected at the same time as PPG signals. To address this challenge, we introduce PPG2ECGps, an end-to-end, patient-specific deep-learning neural network utilizing the W-Net architecture. This novel approach enables direct ECG signal reconstruction from PPG signals, eliminating the need for signal alignment. Our experiments show that the proposed model achieves mean values of 0.977 mV for Pearson’s correlation coefficient, 0.037 mV for the root mean square error, and 0.010 mV for the normalized dynamic time-warped distance when comparing reconstructed ECGs to reference ECGs from a dataset of 500 records. As PPG signals are more accessible than ECG signals, our proposed model has significant potential to improve patient monitoring and diagnosis in healthcare settings via wearable devices.