Smart Healthcare Engineering Management and Risk Analytics by Shuai Ding & Desheng Wu & Luyue Zhao & Xueyan Li

Author:Shuai Ding & Desheng Wu & Luyue Zhao & Xueyan Li
Language: eng
Format: epub
ISBN: 9789811925603
Publisher: Springer Nature Singapore

In this section, PPG signals were obtained from face and palm videos, and the feature extraction of multi-channel signals was carried out on the basis of existing research according to the PPG morphology, and the basic physiological parameters including age, gender, height, weight, and BMI were used as the input of the classification model. The XGBoost binary model was used to detect the risk of hypertension, and the importance of each feature was analyzed to draw relevant conclusions. This study provides an interpretable and supervised objective assessment method for societal blood pressure management and hypertension risk management in special medical scenarios, and proves the validity of the characteristics, enabling real-time hypertension risk analysis. The structure of the method is listed in Fig. 6.2:

Fig. 6.2Framework for non-contact hypertension risk analysis based on multiplex rPPG signal

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