Normalization and Comparison of Photoplethysmography Between Normal and Patient Groups Using Deep Neural Networks

Abstract Photoplethysmography (PPG) is easy to perform and provides a variety of measurements, including details of heart rate and arrhythmia. However, automated PPG methods have not been developed because of their susceptibility to motion artifacts and differences in waveform characteristics among individuals. With increasing use of telemedicine, there is growing interest in application of deep neural network (DNN) technology for efficient analysis of vast amounts of PPG data. This study proposes an automatic algorithm incorporating DNNs for individual and patient-group identification; this is achieved by selecting normally measured waveforms, deleting error regions, and normalizing the pulse wave to obtain 10 “section values” that can be easily compared to other waveforms. The proposed algorithm was able to distinguish between patients aged 60–75 years with diabetes and hypertension and healthy subjects aged 25–35 years (AUC = 0.998). On the other hand, errors were frequently observed in identification of individuals (AUC = 0.819).