Deep learning algorithm evaluation of hypertension classification in less photoplethysmography signals conditions. (March 2021)

• Record Type: Journal Article
• Authors: Yen, Chih-Ta; Chang, Sheng-Nan; Liao, Cheng-Hong
• Is Part Of: Measurement and control. Volume 54:Number 3/4(2021)
• Page Start: 439
• Page End: 445
• Publication Date: 2021-03
• Subjects: Photoplethysmography -- hypertensive -- deep learning -- residual network convolutional neural network -- bidirectional long short-term memory; Automatic control -- Periodicals; Engineering instruments -- Periodicals; Production engineering -- Periodicals; 629.8
• DOI: 10.1177/00202940211001904
• Languages: English
• ISSNs: 0020-2940

Abstract

This study used photoplethysmography signals to classify hypertensive into no hypertension, prehypertension, stage I hypertension, and stage II hypertension. There are four deep learning models are compared in the study. The difficulties in the study are how to find the optimal parameters such as kernel, kernel size, and layers in less photoplethysmographyt (PPG) training data condition. PPG signals were used to train deep residual network convolutional neural network (ResNetCNN) and bidirectional long short-term memory (BILSTM) to determine the optimal operating parameters when each dataset consisted of 2100 data points. During the experiment, the proportion of training and testing datasets was 8:2. The model demonstrated an optimal classification accuracy of 76% when the testing dataset was used.