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Biosensors 2018, 8(4), 101; https://doi.org/10.3390/bios8040101

Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification

1
School of Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China
2
School of Electrical and Computer Engineering, University of British Columbia, BC V6T 1Z4, Canada
3
Faculty of Medicine, University of British Columbia, BC V1Y 1T3, Canada
4
BC Children’s and Women’s Hospital, Vancouver, BC V6H 3N1, Canada
*
Author to whom correspondence should be addressed.
Received: 28 August 2018 / Revised: 10 October 2018 / Accepted: 14 October 2018 / Published: 26 October 2018
(This article belongs to the Special Issue Smart Biomedical Sensors)
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Abstract

Blood pressure is a basic physiological parameter in the cardiovascular circulatory system. Long-term abnormal blood pressure will lead to various cardiovascular diseases, making the early detection and assessment of hypertension profoundly significant for the prevention and treatment of cardiovascular diseases. In this paper, we investigate whether or not deep learning can provide better results for hypertension risk stratification when compared to the classical signal processing and feature extraction methods. We tested a deep learning method for the classification and evaluation of hypertension using photoplethysmography (PPG) signals based on the continuous wavelet transform (using Morse) and pretrained convolutional neural network (using GoogLeNet). We collected 121 data recordings from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Database, each containing arterial blood pressure (ABP) and photoplethysmography (PPG) signals. The ABP signals were utilized to extract blood pressure category labels, and the PPG signals were used to train and test the model. According to the seventh report of the Joint National Committee, blood pressure levels are categorized as normotension (NT), prehypertension (PHT), and hypertension (HT). For the early diagnosis and assessment of HT, the timely detection of PHT and the accurate diagnosis of HT are significant. Therefore, three HT classification trials were set: NT vs. PHT, NT vs. HT, and (NT + PHT) vs. HT. The F-scores of these three classification trials were 80.52%, 92.55%, and 82.95%, respectively. The tested deep method achieved higher accuracy for hypertension risk stratification when compared to the classical signal processing and feature extraction method. Additionally, the method achieved comparable results to another approach that requires electrocardiogram and PPG signals. View Full-Text
Keywords: pulse morphology; pulse oximeter; blood pressure monitoring; pulse arrival time; global health; digital medicine; wearable devices; hypertension assessment; hypertension evaluation pulse morphology; pulse oximeter; blood pressure monitoring; pulse arrival time; global health; digital medicine; wearable devices; hypertension assessment; hypertension evaluation
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Liang, Y.; Chen, Z.; Ward, R.; Elgendi, M. Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification. Biosensors 2018, 8, 101.

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