Estimating Blood Pressure via Artificial Neural Networks Based on Measured Photoplethysmography Waveforms

K. N.G. Priyanka, Chang-Po Chao, Tse Yi Tu, Yung Hua Kao, Ming Hua Yeh, Rajeev Pandey, Fitrah P. Eka

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)


A new approach for estimating blood pressure from photoplethysmography (PPG) signals is developed using artificial neural networks (ANNs). Blood Pressure is one of the most important parameters that can provide valuable information of personal healthcare. A reflective photoplethysmography (PPG) sensor module is developed for the cuffless, non-invasive blood pressure (BP) measurement based on PPG at wrist on radial artery. Blood Pressure is in a relation with the pulse duration of the PPG. In this paper, we propose to estimate blood pressure from PPG signal by using artificial neural networks approach. This is the first reported study to consider varied temporal periods of PPG waveforms as features for application of artificial neural networks (ANNs) to estimate blood pressure. We compared our results with those measured using a commercial cuff-based digital blood pressure measuring device and obtained encouraging results of overall SBP and DBP regression (R) as 0.99115.

主出版物標題2018 IEEE SENSORS, SENSORS 2018 - Conference Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
出版狀態Published - 26 十二月 2018
事件17th IEEE SENSORS Conference, SENSORS 2018 - New Delhi, India
持續時間: 28 十月 201831 十月 2018


名字Proceedings of IEEE Sensors


Conference17th IEEE SENSORS Conference, SENSORS 2018
城市New Delhi

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