Development of a novel light-sensitive PPG model using PPG scalograms and PPG-NET learning for non-invasive hypertension monitoring

作者：A. Al-Fahoum, Ahmad Al Omari, Ghadeer Al Omari, Ala'a Zyout · 发表于：Heliyon · 年份：2024 · DOI：10.1016/j.heliyon.2024.e39745 · 被引用次数：26 · 研究领域：Medicine

Background and objective Photoplethysmography (PPG) signals provide a non-invasive method for monitoring cardiovascular health, including blood pressure levels, which are critical for the early detection and management of hypertension. This study leverages wavelet transformation and special purpose deep learning model, enhanced by signal processing and normalization, to classify blood pressure stages from PPG signals. The primary objective is to advance non-invasive hypertension monitoring, improving the accuracy and efficiency of these assessments. Methods The study employed continuous wavelet transform (CWT) to prepare PPG signals for analysis using a special purpose PPG-NET designed by applying advanced deep-learning models. PPG-NET was verified by applying several pre-trained models, including Inception, MobileNetV2, InceptionResNetV2, and others to the PPG data. Rigorously five-fold cross-validated models were conducted to obtain the models' performance to ensure robustness and repeatability of results. Results The PPG-NET model demonstrated superior performance, achieving a perfect accuracy of 100 % in classifying the four stages of hypertension—normal, prehypertension, stage 1, and stage 2. The evaluation metrics reported include precision, sensitivity, and specificity, with the PPG-NET model achieving 100 % across all metrics. Other models showed varying levels of accuracy, with InceptionV3 also reaching 91.5 %, while some, like VGG-19, underperformed significantly. C...