Hemorrhagic risk prediction in coronary artery disease patients based on photoplethysmography and machine learning

• Published in: Scientific reports 2022-11, Vol.12 (1), p.19190-19190, Article 19190
• Main Authors: He, Zhengling, Zhang, Huajun, Chen, Xianxiang, Shi, Junshan, Bai, Lu, Fang, Zhen, Wang, Rong
• Format: Article
• Language: English
• Subjects: 631/114/1305; 631/114/2164; 692/700/139; Cardiovascular disease; Coronary Artery Disease; Coronary vessels; Data acquisition; Heart diseases; Hemorrhage; Humanities and Social Sciences; Humans; Learning algorithms; Logistic Models; Machine Learning; multidisciplinary; Patients; Photoplethysmography - methods; Regression analysis; Science; Science (multidisciplinary); Sensitivity and Specificity; Vein & artery diseases
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-22719-7

Hemorrhagic events are the main focus of attention during antithrombosis therapy in patients with coronary artery disease (CAD). This study aims to investigate the potential of using photoplethysmography (PPG) and machine learning techniques to assess hemorrhagic risk in patients with CAD. A total of 1638 patients with CAD were enrolled from January 2018 to October 2019, among which 114 patients were observed to have at least one positive event. Importantly, 102 patients with 9933 records were finally retained for analysis in this study. Participants were required to collect data using the portable PPG acquisition device and the specially designed Android APP. The data was collected and uploaded to a remote server. Based on collected PPG signals, we extracted features in a total of 30 dimensions from time-domain, frequency-domain, and wavelet packet decomposition. Logistic regression, support vector regression, random forest, and XGBoost regression models were established to achieve hemorrhagic risk evaluation, and then, their performances were compared. In total, 10 features extracted from PPG showed statistical significance ( p