Blood Pressure Estimation from PPG Signals - ICASSP 2020

Blood Pressure Estimation from PPG Signals Using Convolutional Neural Networks and Siamese Network - ICASSP 2020
https://ieeexplore.ieee.org/iel7/9040208/9052899/09053446.pdf

Blood pressure (BP) is a vital sign of the human body and an important parameter for early detection of cardiovascular diseases. It is usually measured using cuff-based devices or
monitored invasively in critically-ill patients. This paper presents two techniques that enable continuous and noninvasive cuff-less BP estimation using photoplethysmography (PPG) signals with Convolutional Neural Networks (CNNs). The first technique is calibrationfree. The second technique achieves a more accurate measurement by estimating BP changes with respect to a patient's PPG and ground truth BP values at calibration time. For this purpose, it uses Siamese network architecture. When trained and tested on the MIMIC-II database, it achieves mean absolute difference in the systolic and diastolic BP of 5.95 mmHg and 3.41 mmHg respectively. These results almost comply with the AAMI recommendation and are as accurate asthe values estimated by many home BP measuring
devices.

Oded Schlesinger, Nitai Vigderhouse, Danny Eytan, Yair Moshe
Signal and Image Processing Laboratory (SIPL)
Andrew and Erna Vitrbi Faculty of Electrical Engineering
Technion – Israel Institute of Technology

Follow this link to learn more: http://sipl.technion.ac.il/

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