Non-constrained blood pressure monitoring using ECG and PPG for personal healthcare.
Blood pressure (BP) is one of the important vital signs that need to be monitored for personal healthcare. Arterial blood pressure (BP) was estimated from pulse transit time (PTT) and PPG waveform. PTT is a time interval between an R-wave of electrocardiography (ECG) and a photoplethysmography (PPG) signal. This method does not require an aircuff and only a minimal inconvenience of attaching electrodes and LED/photo detector sensors on a subject. PTT computed between the ECG R-wave and the maximum first derivative PPG was strongly correlated with systolic blood pressure (SBP) (R = -0.712) compared with other PTT values, and the diastolic time proved to be appropriate for estimation diastolic blood pressure (DBP) (R = -0.764). The percent errors of SBP using the individual regression line (4-11%) were lower than those using the regression line obtained from all five subjects (9-14%). On the other hand, the DBP estimation did not show much difference between the individual regression (4-10%) and total regression line (6-10%). Our developed device had a total size of 7 x 13.5 cm and was operated by single 3-V battery. Biosignals can be measured for 72 h continuously without external interruptions. Through a serial network communication, an external personal computer can monitor measured waveforms in real time. Our proposed method can be used for non-constrained, thus continuous BP monitoring for the purpose of personal healthcare.
Yoon
Youngzoon
Y
Institute for Biomedical Electronics, Seoul National University of Technology, Seoul, South Korea.
Cho
Jung H
JH
Yoon
Gilwon
G
D000328:Adult; D001794:Blood Pressure / Q000502:physiology; D018660:Blood Pressure Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D015924:Blood Pressure Monitors; D004562:Electrocardiography / Q000295:instrumentation* / Q000379:methods; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D008297:Male; D011674:Pulse; D012039:Regional Blood Flow / Q000502:physiology; D012044:Regression Analysis
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