• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.6
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• pp.1095-1101
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• 2008

The desirable method to diagnose abnormal blood pressure is to measure and manage blood pressure continuously and regularly. However, the sphygmomanometers that are based on a cuff have faults in that they can not measure the blood pressure continuously and they cause an unpleasant feeling. Therefore, it is essential to develop a new measuring method that causes no pain and that can obtain blood pressure continuously without any unpleasant feeling. Thus, we propose here a regression method to estimate the systolic blood pressure by using the PTTL(pulse transit time on leg) with some body parameters which are chosen from the relational analysis with systolic blood pressure. The data we use to make the regression model were obtained in triplicate from each of 50 males who were from 18 to 35 years. And we made estimation experiments of blood pressure on 10 males who did not take part in the making the regression model. According to the results, the proposed method showed a mean error of 4.00 mmHg and the standard variance was 2.45 mmHg. When we comparing the results of the proposed method with the rule of American National Standards Institute of the Association of the Advancement of Medical Instruments(ANSI/AAMI), the results satisfied the rule of a mean error less than 5 mmHg and a standard variance less than 8 mmHg. Therefore we were able to validate the usefulness of the proposed method.

• 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.