期刊
IEEE SENSORS JOURNAL
卷 20, 期 17, 页码 10000-10011出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2020.2990864
关键词
Biomedical monitoring; Heart rate; Deep learning; Sensors; Monitoring; Estimation; Feature extraction; Heart rate; blood pressure; deep learning; long-term recurrent convolutional network (LRCN); Photoplethysmography (PPG); times-series prediction
资金
- SMDP-C2S program of the Ministry of Electronics and Information Technology (MEITY), Government of India
- MEITY
This paper presents a deep learning model 'PP-Net' which is the first of its kind, having the capability to estimate the physiological parameters: Diastolic blood pressure (DBP), Systolic blood pressure (SBP), and Heart rate (HR) simultaneously from the same network using a single channel PPG signal. The proposed model is designed by exploiting the deep learning framework of Long-term Recurrent Convolutional Network (LRCN), exhibiting inherent ability of feature extraction, thereby, eliminating the cost effective steps of feature selection and extraction, making less-complex for deployment on resource constrained platforms such as mobile platforms. The performance demonstration of the PP-Net is done on a larger and publically available MIMIC-II database. We achieved an average NMAE of 0.09 (DBP) and 0.04 (SBP) mmHg for BP, and 0.046 bpm for HR estimation on total population of 1557 critically ill subjects. The accurate estimation of HR and BP on a larger population compared to the existing methods, demonstrated the effectiveness of our proposed deep learning framework. The accurate evaluation on a huge population with CVD complications, validates the robustness of the proposed framework in pervasive healthcare monitoring especially cardiac and stroke rehabilitation monitoring.
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