• Title/Summary/Keyword: 맥파 차동값

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Digital Blood Pressure Estimation with the Differential Value of the Arterial Pulse Waveform (맥파의 차동값에 의한 디지털 방식의 혈압 추정 기법)

  • Kim, Boyeon;Chang, Yunseok
    • KIPS Transactions on Computer and Communication Systems
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    • v.5 no.6
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    • pp.135-142
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    • 2016
  • We proposed the new method to estimate the blood pressure with the differential value of the digital arterial pulse waveform and BP relation equation. To get the digital arterial pulse waveform, we use the arterial pulse waveform measurement system that has digital air-pressure sensor device and smart phone. The acquired digital arterial pulse waveforms are classified as hypertension group, normal group, and hypotension group, and we can derive the average differential value between the highest point and lowest point of a single waveform of individuals along with the group. In this study, we found the functional correlation between the blood pressure and differential value as a form of BP relation equation through the regression process on the average of differential value and blood pressure value from a tonometer. The Experimental results show the BP relation equation can give easy blood pressure estimation method with a high accuracy. Although this estimation method has over 66 % error rate and does not give the high level of the accuracy for the diastolic compares to the commercial tonometer, the estimation results for the systolic show the high accuracy that has less than 10 % error rate.

Cuffless Blood Pressure Estimation Based on a Convolutional Neural Network using PPG and ECG Signals for Portable or Wearable Blood Pressure Devices (휴대용 및 웨어러블 측정기를 위한 ECG와 PPG 신호를 활용한 합성곱 신경망 알고리즘 기반의 비가압식 혈압 추정 방법)

  • Cho, Jinwoo;Choi, Ahyoung
    • Journal of Korea Society of Industrial Information Systems
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    • v.25 no.3
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    • pp.1-10
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    • 2020
  • In this paper, we propose an algorithm for estimating blood pressure using ECG (Electrocardiogram) and PPG (Photoplethysmography) signals. To estimate the BP (Blood pressure), we generate a periodic input signal, remove the noise according to the differential and threshold methods, and then estimate the systolic and diastolic blood pressures based on the convolutional neural network. We used 49 patient data of 3.1GB in the MIMIC database. As a result, it was found that the prediction error (RMSE) of systolic BP was 5.80mmHg, and the prediction error of diastolic BP was 2.78mmHg. This result confirms that the performance of class A is satisfied with the existing BP monitor evaluation method proposed by the British High Blood Pressure Association.