Toward Hypertension Prediction Based on PPG-Derived HRV Signals: a Feasibility Study

Heart rate variability (HRV) is often used to assess the risk of cardiovascular disease, and data on this can be obtained via electrocardiography (ECG). However, collecting heart rate data via photoplethysmography (PPG) is now a lot easier. We investigate the feasibility of using the PPG-based heart...

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Bibliographic Details
Published in:Journal of medical systems 2018-06, Vol.42 (6), p.103-7, Article 103
Main Authors: Lan, Kun-chan, Raknim, Paweeya, Kao, Wei-Fong, Huang, Jyh-How
Format: Article
Language:English
Subjects:
Cardiovascular diseases
Data mining
Echocardiography
EKG
Electrocardiography
Feasibility studies
Frequency domain analysis
Health Informatics
Health risks
Health Sciences
Heart rate
Hypertension
Medicine
Medicine & Public Health
Mobile & Wireless Health
Parameter identification
Predictions
Statistics for Life Sciences
Telemedicine
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Summary:Heart rate variability (HRV) is often used to assess the risk of cardiovascular disease, and data on this can be obtained via electrocardiography (ECG). However, collecting heart rate data via photoplethysmography (PPG) is now a lot easier. We investigate the feasibility of using the PPG-based heart rate to estimate HRV and predict diseases. We obtain three months of PPG-based heart rate data from subjects with and without hypertension, and calculate the HRV based on various forms of time and frequency domain analysis. We then apply a data mining technique to this estimated HRV data, to see if it is possible to correctly identify patients with hypertension. We use six HRV parameters to predict hypertension, and find SDNN has the best predictive power. We show that early disease prediction is possible through collecting one’s PPG-based heart rate information.
ISSN:0148-5598
1573-689X
DOI:10.1007/s10916-018-0942-5