Hypertension Risk Assessment from Photoplethysmographic Recordings Using Deep Learning Classifiers

Regular monitoring of blood pressure (BP) is essential for early detection of cardiovascular diseases caused by hypertension, a potentially deadly condition without symptoms in its first stages. This study investigates whether deep learning techniques can assess risk levels of BP using only photoplethysmographic (PPG) recordings without the need of electrocardiographic (ECG) recordings, as in many previous studies. 15.240 segments from 50 different patients containing simultaneous PPG and arterial blood pressure (ABP) signals were analysed. GoogleNet and ResNet pretrained convolutional neural networks (CNN) with the scalogram of PPG signals obtained by continuous wavelet transform (CWT) used as input images were employed for the classification. The highest F1 score was achieved by discriminating normotensive (NT) patients from prehypertensive (PH) and hypertensive (HT), being 92.10% for GoogleNet and 93.91% for ResNet, respectively. In addition, intra-patient classification using different data segments for training and validation provided an F1 score of 90.28% with GoogleNet and 89.04% with ResNet. Time frequency transformation of PPG recordings to feed deep learning classifiers has been able to provide outstanding results in hypertension risk assessment without requiring either ECG recordings or feature extraction.