Key result
A random forest model using camera-based imaging photoplethysmography characteristics predicted cardiovascular disease status with an area under the curve of up to 0.975.
Why the study?
Noninvasive, convenient, and reliable cardiovascular health assessment using pervasive healthcare applications can aid in preventing cardiovascular diseases.
Can camera-based imaging photoplethysmography (iPPG) characteristics accurately detect cardiovascular health states such as a history of coronary heart disease or hypertension?
Observational (n=226)
Can camera-based imaging photoplethysmography (iPPG) characteristics accurately detect cardiovascular health states such as a history of coronary heart disease or hypertension?
Camera-based imaging photoplethysmography combined with random forest modeling shows high accuracy in detecting cardiovascular health states, offering a potential noncontact tool for CVD assessment.
May enable convenient camera-based CVD screening; hypothesis-generating and requires prospective validation before clinical adoption.
Noninvasive, convenient, and reliable cardiovascular health assessment using pervasive healthcare applications can help in preventing cardiovascular diseases (CVDs). This study proposes a novel cardiovascular health state detection procedure based on the multiple imaging photoplethysmography (iPPG) characteristics collected by camera-based iPPG monitoring. In this study, we collect a dataset from 226 subjects, derive 20 iPPG characteristic variables, and construct a two-way classification to predict whether subjects are CVDs relevant patients with a history of coronary heart disease or hypertension. The cross-validation accuracy of full-feature random forest (RF) model is 0.860 in training set and weighted F1-score and area under curve (AUC) 0.900 and 0.7831 in test set, respectively. The contribution ratio of heart rate variability (HRV) and iPPG average waveform characteristics is 3:2. Sample equalization can improve the model performance and increase AUC to 0.975, with the contribution ratio of HRV and iPPG average waveform characteristics remaining consistent as 7:5. The proposed scheme emphasizes the advantages of using noncontact and high-dimensional features of camera-based iPPG healthcare approach for auxiliary assessment. The effective use of iPPG full-features and RF modeling has a strong application significance for the identification of the cardiovascular health of status and is hoped to further contribute to the prevention and intervention of CVDs.
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Luo et al. (2023) conducted an observational in Cardiovascular diseases (coronary heart disease or hypertension) (n=226). Camera-based imaging photoplethysmography (iPPG) monitoring was evaluated on Prediction of cardiovascular disease status (history of coronary heart disease or hypertension). A random forest model using camera-based imaging photoplethysmography characteristics predicted cardiovascular disease status with an area under the curve of up to 0.975.
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