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...
| Publicado en: | Journal of Medical Systems Vol. 42; no. 6; pp. 1 - 2 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129928828&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129928828 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jun2018 vid: 42 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 129928828 129928828 129928828 10.1007/s10916-018-0942-5 129928828 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Toward Hypertension Prediction Based on PPG-Derived HRV Signals: a Feasibility Study. aug: au: Lan, Kun-chan Raknim, Paweeya Kao, Wei-Fong Huang, Jyh-How affil: School of Chinese Medicine, China Medical University, Taichung, Taiwan sug: subj: Hypertension Diagnosis Heart Rate Variability Plethysmography Data Mining Monitoring, Physiologic Wearable Sensors Middle Age Aged Aged, 80 and Over Machine Learning Taiwan Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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