Identification of blood pressure reflecting personalized traits using bilateral photoplethysmography
| Autor: | Young-Suk Shin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Normalization (statistics)
Adult Male medicine.medical_specialty linear discriminant analysis 0206 medical engineering Biomedical Engineering Biophysics Diastole personalized trait Health Informatics Bioengineering 02 engineering and technology maximum low amplitudes Prehypertension Biomaterials 03 medical and health sciences Young Adult 0302 clinical medicine Photoplethysmogram Internal medicine medicine Image Processing Computer-Assisted Humans Mathematics Aged Discriminant Analysis Blood Pressure Determination Middle Aged Linear discriminant analysis 020601 biomedical engineering Blood pressure Hypertension Cardiology photoplethysmography Female 030217 neurology & neurosurgery Algorithms Information Systems Research Article |
| Zdroj: | Technology and Health Care |
| ISSN: | 1878-7401 0928-7329 |
| Popis: | BACKGROUND: Blood pressure (BP) is currently diagnosed by cuff-based devices, which are inconvenient and provide discontinuous measurements. Photoplethysmography (PPG)-based cuffless techniques have recently been used to accurately estimate both systolic BP (SBP) and diastolic BP (DBP). However, it is difficult to use these SBP and DBP estimations to reflect the personalized traits in the peripheral vascular condition; thus, their accuracy is limited. OBJECTIVE: The purpose of this study is to describe a technique that can be distinguished simply among three BP categories (normotensive, prehypertensive, and hypertensive) and reflect individual traits using PPG only. METHODS: We measured BP over 120 s using the fingers of 105 subjects. The PPG waveforms varied in size and amplitude over time. Therefore, normalization for uniform features for individual traits was done after the extracted waveforms were divided into multiple windows. The feature is determined by the lowest amplitude in the waveform within each divided window. The features have been applied to distinguish three BP categories using the first-eigenvector (1-EV) and second-eigenvector (2-EV) in linear discriminant analysis. RESULTS: The best decision boundary for each BP category was estimated using 1-EV (-0.02 to +0.02) and 2-EV (>+0.02) in the hypertensive category, 1-EV (< 0) and 2-EV (⩽+0.02) in the prehypertensive category, and 1-EV (⩾-0.02) and 2-EV (⩽+0.02) in the normotensive category. The overlap range with 1-EV (-0.02 to 0) and 2-EV (⩽+0.02) in particular accurately reflected individual traits. CONCLUSION: Discrimination among the three BP categories reflecting individual traits was successfully achieved using PPG. This method could improve limitations of cuff-based techniques. |
| Databáze: | OpenAIRE |
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