Affective Computing Based on Morphological Features of Photoplethysmography for Patients with Hypertension.

ABSTRACT: Negative and positive emotions are the risk and protective factors for the cause and prognosis of hypertension. This study aimed to use five photoplethysmography (PPG) waveform indices and affective computing (AC) to discriminate the emotional states in patients with hypertension. Forty-three patients with essential hypertension were measured for blood pressure and PPG signals under baseline and four emotional conditions (neutral, anger, happiness, and sadness), and the PPG signals were transformed into the mean standard deviation of five PPG waveform indices. A support vector machine was used as a classifier. The performance of the classifier was verified by using resubstitution and six-fold cross-validation (CV) methods. Feature selectors, including full search and genetic algorithm (GA)

SUBMITTER: Yu SN 

PROVIDER: S-EPMC9698908 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature