PP-Net accurately estimated physiological parameters from single-channel PPG signals, achieving an average NMAE of 0.09 mmHg for DBP, 0.04 mmHg for SBP, and 0.046 bpm for HR in 1557 subjects.
Does the PP-Net deep learning framework accurately estimate blood pressure and heart rate from single-channel PPG signals in critically ill subjects?
The PP-Net deep learning framework provides highly accurate, simultaneous estimation of blood pressure and heart rate from a single-channel PPG signal, demonstrating potential for pervasive healthcare monitoring.
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.
Panwar et al. (Thu,) conducted a other in Critically ill subjects with CVD complications (n=1,557). PP-Net (Long-term Recurrent Convolutional Network) vs. Existing methods was evaluated on Normalized Mean Absolute Error (NMAE) for DBP, SBP, and HR estimation. PP-Net accurately estimated physiological parameters from single-channel PPG signals, achieving an average NMAE of 0.09 mmHg for DBP, 0.04 mmHg for SBP, and 0.046 bpm for HR in 1557 subjects.
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