• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.67-73
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• 2019

The blood pressure measurement is calculated as a value corresponding to the pressure of the blood vessel using the pressure from the outside for a long time. Due to the recent miniaturization of measurement equipment and the ICT combination of personal healthcare systems, a system that enables continuous and real-time measurement of blood pressure with a sensor is required. In this study, blood pressure was measured using pulse transit time using Photoplethysmography. In this study, blood pressure was estimated by using systolic blood pressure. And it is possible to make measurement only with PPG itself, which can contribute to making a micro blood pressure measuring device. As a result, systolic blood pressure and PPG's S1-P and P-S2 were used to analyze the possibility of blood pressure estimation.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.18-26
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• 2015

Measuring the heart rate during exercise is important to properly control the amount of exercise. With the recent advent of smart device usage, there is a dramatic increase in interest in devices for the real-time measurement of the heart rate during exercise. During intensive exercise, accurate heart rate estimation from wrist-type photoplethysmography (PPG) signals is a very difficult problem due to motion artifact (MA). In this study, we propose an efficient algorithm for an accurate estimation of the heart rate from wrist-type PPG signals. For the twelve data sets, the proposed algorithm achieves the average absolute error of 1.38 beat per minute (BPM) and the Pearson correlation between the estimates and the ground-truth of heart rate was 0.9922. The proposed algorithm presents the strengths in an accurate estimation together with a fast computation speed, which is attractive in application to wearable devices.

• Journal of Korea Multimedia Society
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• v.24 no.11
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• pp.1481-1491
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• 2021

Since most biosignals rely on contact-based measurement, there is still a problem in that it is hard to provide convenience to users by applying them to daily life. In this paper, we present a mobile application for estimating heart rate based on a deep learning model. The proposed application measures heart rate by capturing real-time face images in a non-contact manner. We trained a three-dimensional convolutional neural network to predict photoplethysmography (PPG) from face images. The face images used for training were taken in various movements and situations. To evaluate the performance of the proposed system, we used a pulse oximeter to measure a ground truth PPG. As a result, the deviation of the calculated root means square error between the heart rate from remote PPG measured by the proposed system and the heart rate from the ground truth was about 1.14, showing no significant difference. Our findings suggest that heart rate measurement by mobile applications is accurate enough to help manage health during daily life.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.3 s.309
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• pp.60-66
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• 2006

The purpose of this study was to suggest a new detection method for early diagnosing diabetic neuropathic foot by obtaining a ratio of toe to finger blood flow using photoplethysmography(PPG) and Laser Doppler(LD). Nerve conduction velocity (NCV) has been routinely used for diagnosing neuropathic foot, but it applies strong electric stimulus to peripheries resulting in stress and pain. The blood flow ratio of 50 neuropathic diabetes($0.96{\pm}0.20$) was significantly higher than that of 64 normal person($0.46{\pm}0.15$)(p<0.000). It also showed that toe temperature of neuropathic diabetes($30.5{\pm}1.4^{\circ}C$) was significantly higher than that of normal group($29.3{\pm}2.0^{\circ}C$)(p<0.000). The optimal boundary value of the blood flow ratio was found to be 0.678 and the sensitivity and specificity of this proposed method resulted in 95.3% and 95.3% respectively. Lastly, there were no neuropathic diabetes whose temperature difference between finger and toe was higher than $4.5\;^{\circ}C$.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.209-215
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• 2019

The most important personal health care in digital health care is a very important issue mainly for chronic diseases. Therefore, it is important to develop a simple wearable device for real-time health management. Existing blood pressure estimation wearable devices use PPG characteristics to analyze PTT and propose blood pressure estimation algorithms. However, the influencing factors of the algorithm such as the reproducibility of PPG, whether to apply various PTTs, and variables generated from the physical differences of the measurers are actually very complex. Therefore, in this study, the correlation between PTT, SBP, and DBP was analyzed, and it was designed to use PPG sensors for device miniaturization. The blood pressure estimation algorithm took into account differences in PPG, heart rate, and personal variables.

• Journal of Digital Policy
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• v.2 no.4
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• pp.25-32
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• 2023

This paper is a study to develop a deep neural network (DNN) blood glucose prediction model based on heart rate (HR) and heart rate variability (HRV) data measured by PPG-based sensors. MLP deep learning consists of an input layer, a hidden layer, and an output layer with 11 independent variables. The learning results of the blood glucose prediction model are MAE=0.3781, MSE=0.8518, and RMSE=0.9229, and the coefficient of determination (R²) is 0.9994. The study was able to verify the feasibility of glycemic control using non-blood vital signs using PPG-based digital devices. In conclusion, a standardized method of acquiring and interpreting PPG-based vital signs, a large data set for deep learning, and a study to demonstrate the accuracy of the method may provide convenience and an alternative method for blood glucose management in dogs.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.962-968
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• 2018

In this study, we developed novel indicators to assess postoperative pain based on PPG derivative waveform. As the candidate indicator of postoperative pain assessment, the time from the start of beating to the n-th peak($T_n$) and the n-th peak amplitude($A_n$) of the PPG derivative were selected. In order to verify derived indicators, each candidate indicator was derived from the PPG of 78 subjects before and after surgery, and it was confirmed whether significant changes were observed after surgery. Logistic classification was performed with each proposed indicator to calculate the pain classification accuracy, then the classification performance was compared with SPI(Surgical Pleth Index, GE Healthcare, Chicago, US). The results showed that there were significant differences(p < 0.01) in all indicators except for $T_3$ and $A_3$. The coefficient of variation(CV) of every time-related indicators were lower than the CV of SPI(30.43%), however, the CV in amplitude-related parameters were higher than that of SPI. Among the candidate indicators, amplitude of the first peak, $A_1$, showed that highest accuracy in post-operative pain classification, 68.72%, and it is 15.53% higher than SPI.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.6
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• pp.794-798
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• 2018

There is a growing need for a care system that can continuously monitor, manage and effectively relieve stress for modern people. In recent years, mobile healthcare devices capable of measuring heart rate have become popular, and many stress monitoring techniques using heart rate variability analysis have been actively proposed and commercialized. In addition, respiratory biofeedback methods are used to provide stress relieving services in environments using mobile healthcare devices. In this case, breathing information should be measured well to assess whether the user is doing well in biofeedback training. In this study, we extracted the heart beat interval signal from the PPG and used the oscillator based notch filter based on the IIR band pass filter to track the strongest frequency in the heart beat interval signal. The respiration signal was then estimated by filtering the heart beat interval signal with this frequency as the center frequency. Experimental results showed that the number of breathing could be measured accurately when the subject was guided to take a deep breath. Also, in the timeing measurement of inspiration and expiration, a time delay of about 1 second occurred. It is expected that this will provide a respiratory biofeedback service that can assess whether or not breathing exercise are performed well.

• Annals of Clinical Neurophysiology
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• v.25 no.1
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• pp.32-37
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• 2023

Background: Type 2 diabetic mellitus (T2DM) is an emerging global pandemic which is associated with lots of co-morbidities and reported vascular dysfunctions. T2DM associated vascular dysfunctions leads to vasculopathy in the form of altered peripheral vascular dynamics. Cold stress test (CST) is a reliable sympathetic reactivity test used for assessing vascular dysfunctions. In this study we are trying to quantify vascular dysfunctions in T2DM patients non invasively by various parameters of photoplethysmography (PPG) of cold stress test. Methods: Case control study had done in referral health center AIIMS, Raipur. Parameters are recorded by finger-PPG before, during and after CST (1 min) in 2 groups, control (n = 20 healthy volunteers) and case (n = 20 diagnosed T2DM patients). Results: Due to cold stress, PPG parameter peak amplitude was significantly decreased in both healthy and T2DM groups (p <0.001 and p <0.001, respectively). However, recovery trend of amplitude was significantly slow in T2DM compared to healthy subjects. Another PPG parameter peak to peak interval was significantly higher in healthy group compared to T2DM patients. Conclusions: This study showed that T2DM patients has significant deranged pulse volume parameters like amplitude and peak to peak interval can be used to objectively quantify the vasculopathy in T2DM patients by using sympathetic reactivity to cold stress.

• Science of Emotion and Sensibility
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• v.11 no.2
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• pp.235-244
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• 2008

This study is to develop automatic extraction system of continuous blood pressure using ECG (Electrocardiogram) and PPG(Photoplethysmography) for u-health care technology. PTT (Pulse Transit Time) was determined from peak difference between ECG and PPG and its inverse made to get blood pressure. Since the peaks were vulnerable to be contaminated from noise and variation of amplitude, this study developed the adaptive algorithm for peak calculation in any noise condition. The developed method of the adaptive peak calculation was proven to make the standard deviations of PPT decrease to 28% and the detection of noise increase to 18%. Also, the correlation model such as blood pressure = -0.044 $\cdot$ PTT + 133.592 has successfully been determined for predicting the continuous pressure measured without using cuff but with using PPG and ECG, only.