Department of Electrical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan.
Pervasive Artificial Intelligence Research (PAIR) Labs, Hsinchu 300093, Taiwan.
Sensors (Basel). 2022 Nov 13;22(22):8771. doi: 10.3390/s22228771.
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), were used to select effective feature combinations. Traditional statistical analyses only differentiated between the emotional states and baseline, whereas AC achieved 100% accuracy in distinguishing between the emotional states and baseline by using the resubstitution method. AC showed high accuracy rates when used with 10 waveform features in distinguishing the records into two, three, and four classes by applying a six-fold CV. The GA feature selector further boosted the accuracy to 78.97%, 74.22%, and 67.35% in two-, three-, and four-class differentiation, respectively. The proposed AC achieved high accuracy in categorizing PPG records into distinct emotional states with features extracted from only five waveform indices. The results demonstrated the effectiveness of the five indices and the proposed AC in patients with hypertension.
消极情绪和积极情绪是导致高血压发生和预后的危险因素和保护因素。本研究旨在使用五种光电容积脉搏波（PPG）波形指标和情感计算（AC）来区分高血压患者的情绪状态。对 43 名原发性高血压患者在基线和四种情绪状态（中性、愤怒、快乐和悲伤）下测量血压和 PPG 信号，并将 PPG 信号转换为五个 PPG 波形指标的平均值标准差。使用支持向量机作为分类器。通过使用重采样和六折交叉验证（CV）方法验证分类器的性能。特征选择器，包括全搜索和遗传算法（GA），用于选择有效的特征组合。传统的统计分析仅区分情绪状态和基线，而 AC 通过重采样方法在区分情绪状态和基线方面达到了 100%的准确率。AC 在使用 10 个波形特征通过六折 CV 将记录分为两类、三类和四类时表现出较高的准确率。GA 特征选择器进一步将准确率分别提高到 78.97%、74.22%和 67.35%。所提出的 AC 仅使用五个波形指标中提取的特征，即可将 PPG 记录准确地分类为不同的情绪状态。结果表明，五种指标和所提出的 AC 在高血压患者中具有有效性。
