• The Journal of Korean Medicine
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• v.41 no.2
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• pp.34-42
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• 2020

Background and Objectives: The purpose of this study was to examine the relationship between between one's quality of life (QoL) level and the arterial stiffness estimated by the second derivative of photoplethysmogram (SDPTG) for women patients. Methods: A retrospective chart review was performed on charts of 407 women patients (38.38±11.82 years) who visited Gangdong Kyung Hee Hospital between April 1st and September 30th, 2011. Vascular aging index (VAI, (b-c-d)/a), b/a, c/a, and d/a were considered as the arterial stiffness indexes, and the Korean version of the Short-Form 36 (SF-36) were completed to estimate one's physical and mental QoL. Results: Physical and mental components of the SF-36 in older group (50, 60, and 70 years-group) were lower than those in younger group (20 and 30 years-group). Large arterial stiffness-related b/a in older group was higher that in younger group, while small arterial stiffness-related d/a in older group was lower that in younger group. Physical and mental component scores of the SF-36 had the negative correlations with VAI and b/a (r; -0.153~-0.195), while had the positive correlations with c/a and d/a (r; 0.147~0.228). Conclusions: In conclusion, this study suggests that convenient and cost-effective SDPTG test may serve as an auxiliary tool to estimate one's physical and mental QoL.

• Journal of Biomedical Engineering Research
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• v.32 no.2
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• pp.109-117
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• 2011

Assuming that photons absorbed by a vessel do not have acute variations, DC component reflect the basal blood volume (or diameter) before blood pulsation. Vascular stiffness and reflection is influenced by changes in basal blood volume (or diameter). This paper describes analysis of the characteristic variations of vascular stiffness, according to relative variations in DC components of the PPG signal (25-75%). For quantitative analysis, we have used parameters that were proposed previously, reflection and stiffness index, and the second derivative of PPG waveform, b/a and d/a. Significantly, the vascular stiffness and reflections were increased according to increase in DC component of the PPG signal for more than about 3% of baseline values. The systolic blood pressure were increased from $113.1{\times}13.18$ to $116.2{\times}13.319$ mmHg, about 2.76% (r = 0.991, P < 0.001) and the AC component of the PPG signal were decreased from $2.073{\times}2.287$ to $1.973{\times}2.2038$ arbitrary unit, about 5.09% (r = -0.993, P < 0.001). It is separated by DC median and correlation analysis was performed for analyzing vascular characteristics according to instantaneous DC variations. There are significant differences between two correlation coefficients in separated data.

• Journal of Digital Policy
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• v.1 no.2
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• pp.61-69
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• 2022

The paper is a study to develop and verify a blood glucose level prediction model based on biosignals obtained from photoplethysmography (PPG) sensors, ICT technology and data. Blood glucose prediction used the MLP architecture of machine learning. The input layer of the machine learning model consists of 10 input nodes and 5 hidden layers: heart rate, heart rate variability, age, gender, VLF, LF, HF, SDNN, RMSSD, and PNN50. The results of the predictive model are MSE=0.0724, MAE=1.1022 and RMSE=1.0285, and the coefficient of determination (R²) is 0.9985. A blood glucose prediction model using bio-signal data collected from digital devices and machine learning was established and verified. If research to standardize and increase accuracy of machine learning datasets for various digital devices continues, it could be an alternative method for individual blood glucose management.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1431-1437
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• 2016

In mobile healthcare device, when to measure the heart rate by using the PPG signal, its performance is reduced according to the motion artifacts that is the movement of user. This is because the frequency range of motion (0.01-10 Hz) and that of PPG signals overlap. Also, the motion artifacts cannot be rectified by general filters. To solve the problem, this paper proposes a method using filter banks and independent component analysis (ICA). To evaluate the performance of the proposed method, we were artificially applied various movements and compared heart rate errors of the moving average filter and ICA. In the experimental results, heart rate error of the proposed method showed very low than moving average filter and ICA. In this way, it is possible to measure stable heart rate if the proposed method is applied to the healthcare terminal design.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.45-50
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• 2019

When PPG signal is used in mobile healthcare devices, the accuracy of the measured heartbeat decreases from the influence by the movement of the user. The reason is that the frequency band of the noise overlaps the frequency band of the PPG signal. In order to remove these same noises, the methods using frequency analysis method or application of acceleration sensor have been investigated and showed excellent performance. However, in applying these methods to low-cost healthcare devices, it is difficult to apply these methods because of much processing time and sensor's cost. In order to solve these problems, this study proposed the filter design method using an adaptive notch filter and the fuzzy inference system to extract more accurate heart rate in real time and evaluated its performance. As results, it showed better results than the other methods. Based on the results, when applying the proposed method to design the mobile healthcare device, it is possible to measure the heartbeat more accurately in real time.