A Novel Deep Learning-Based Approach for Hypertension Level Detection Using PPG
Sagnik De, Prithwijit Mukherjee, Anisha Halder Roy
Abstract
In the contemporary era, a significant portion of individuals endure cardiovascular ailments (CVDs). Hypertension stands as the principal cause behind blood pressure (BP) irregularities and diverse CVDs. Addressing this exigency, the ceaseless monitoring of BP has emerged as an urgent priority. Our study endeavors to devise an efficacious deep learning-powered automated technique for measuring BP (specifically systolic blood pressure (SBP) and diastolic blood pressure (DBP)), leveraging potentially cost-effective technology. Within our investigation, we have formulated two Long Short-Term Memory (LSTM)-based regression models that prognosticate SBP and DBP based on recorded photoplethysmogram (PPG) readings. Furthermore, an attention mechanism-based TLSTM (tanh Long-Short Term Memory) model has been proposed that can accurately predict distinct stages of hypertension, namely Normal, Pre-Hypertension, Hypertension stage 1, and Hypertension stage 2. The attained root-mean-squared error (RMSE) values are 10.503 and 9.284 for SBP and DBP, respectively, whereas the mean absolute error (MAE) values are 7.529 and 4.218 for SBP and DBP, respectively. The proposed attention mechanism-based TLSTM model exhibits a classification accuracy of 96%. The novelty of this investigation resides in the incorporation of an attention module into the TLSTM network for increasing its classification accuracy.