• 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.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.32 no.1 s.55
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• pp.29-33
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• 2006

This article describes an enzyme stabilization method that allows the use of enzymes irrespective of environmental factors, especially heat, while maintaining their activity for a long time. We have designed enzyme microcapsules that consist of papain enzyme cores, poly(propylene glycol) interlayers, and poly(${\epsilon}-caprolactone$) walls. By confocal laser scanning microscopy measurements and the thermal stability of papain-loaded microcapsules, it is demonstrated that the papain is surrounded by a hydrophobic polyol layer and stabilized by the exclusive volume effect. In our study, improved thermal stability can be obtained by using more hydrophobic long-chained polyols, which is understood to be attributed to the effective formation of a hydrophobic polyol layer between the papain and the polymer wall by means of conformational anchoring in the interface.

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.132-138
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• 2007

MR(magnetic resonance) image of moving organ such as heart shows serious distortion of MR image due to motion itself. To eliminate motion artifacts, MRI(magnetic resonance imaging) scan sequences requires a trigger pulse like ECG(electro-cardiography) R-wave. ECG-gating using cardiac cycle synchronizes the MRI sequence acquisition to the R-wave in order to eliminate image motion artifacts. In this paper, we designed ECG/PPG(photo-plethysmography) gating system which is for eliminating motion artifacts due to moving organ. This system uses nonmagnetic carbon electrodes, lead wire and shield case for minimizing RF(radio-frequency) pulse and gradient effect. Also, we developed a ECG circuit for preventing saturation by magnetic field and a finger plethysmography sensor using optic fiber. And then, gating pulse is generated by adaptive filtering based on NLMS(normalized least mean square) algorithm. To evaluate the developed system, we measured and compared MR imaging of heart and neck with and without ECG/PPG gating system. As a result, we could get a clean image to be used in clinically. In conclusion, the designed ECG/PPG gating system could be useful method when we get MR imaging of moving organ like a heart.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.12 no.1
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• pp.42-62
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• 2008

Objectives: It is well known that some parameters of the photoplethysmogram (PPG) acquired by time domain contour analysis can be used as markers of vascular aging. But the previous studies that have been performed for frequency domain analysis of the PPG to date have provided only restrictive and fragmentary information. The aim of the present investigation was to determine whether the harmonics extracted from the PPG using a fast Fourier transformation could be used as an index of vascular aging. Methods: The PPG was measured in 600 recruited subjects for 30 second durations, To grasp the gross age-related change of the PPG waveform, we grouped subjects according to gender and age and averaged the PPG signal of one pulse cycle. To calculate the conventional indices of vascular aging, we selected the 5-6 cycles of pulse that the baseline was relatively stable and then acquired the coordinates of the inflection points. For the frequency domain analysis we performed a power spectral analysis on the PPG signals for 30 seconds using a fast Fourier transformation and dissociated the harmonic components from the PPG signals. Results: A final number of 390 subjects (174 males and 216 females) were included in the statistical analysis. The normalized power of the harmonics decreased with age and on a logarithmic scale reduction of the normalized power in the third (r=-0.492, P<0.0001), fourth (r=-0.621, P<0.0001) and fifth harmonic (r=-0.487, P<0.0001) was prominent. From a multiple linear regression analysis, Stiffness index, reflection index and corrected up-stroke time influenced the normalized power of the harmonics on a logarithmic scale. Conclusions: The normalized harmonic power decreased with age in healthy subjects and may be less error prone due to the essential attributes of frequency domain analysis. Therefore, we expect that the normalized harmonic power density can be useful as a vascular aging marker.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.7
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• pp.1020-1026
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• 2013

Multirate filtering process on the biological signals like Electrocardiogram (ECG) and Photoplethysmogram (PPG) can be defined as the digital signal processing algorithm in which the sampling rate varies to omit or interpolate the intermediate values between the sampled data. With this aim, we suggest a new multirate filtering algorithm by deleting the extraneous data to eliminate the unwanted degradations such as granular noise due to the usage of high sampling frequency and simultaneously to detect the fiducial features of ECG and PPG with reducing the complexity of resolving fiducial points such as R-peak, Pulse peak and Pulse Transit Time (PTT). After the experimental simulations performed, we can conclude the fact that we can detect the fiducial features of ECG and PPG signal in terms of R-peak, Pulse peak and PTT without the loss of accuracy even if we do not maintain the original sampling frequency.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.103-112
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• 2018

In this paper, we propose a method of recognizing emotions using PPG sensor which measures blood flow according to emotion. From the existing PPG signal, we use a method of determining positive emotions and negative emotions in the frequency domain through PSD (Power Spectrum Density). Based on James R. Russell's two-dimensional prototype model, we classify emotions as joy, sadness, irritability, and calmness and examine their association with the magnitude of energy in the frequency domain. It is significant that this study used the same PPG sensor used in wearable devices to measure the top four kinds of emotions in the frequency domain through image experiments. Through the questionnaire, the accuracy, the immersion level according to the individual, the emotional change, and the biofeedback for the image were collected. The proposed method is expected to be various development such as commercial application service using PPG and mobile application prediction service by merging with context information of existing smart phone.

• Journal of Digital Convergence
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• v.21 no.3
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• pp.33-39
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• 2023

This study is a performance evaluation of a blood sugar monitoring system that combines a PPG sensor, which is an evaluation device for blood glucose monitoring, and a DNN algorithm when monitoring capillary blood glucose. The study is a researcher-led clinical trial conducted on participants from September 2023 to November 2023. PPG-BGMS compared predicted blood sugar levels for evaluation using 1-minute heart rate and heart rate variability information and the DNN prediction algorithm with capillary blood glucose levels measured with a blood glucose meter of the standard personal blood sugar management system. Of the 100 participants, 50 had type 2 diabetes (T2DM), and the average age was 67 years (range, 28 to 89 years). It was found that 100% of the predicted blood sugar level of PPG-BGMS was distributed in the A+B area of the Clarke error grid and Parker(Consensus) error grid. The MARD value of PPG-BGMS predicted blood glucose is 5.3 ± 4.0%. Consequentially, the non-blood-based PPG-BGMS was found to be non-inferior to the instantaneous blood sugar level of the clinical standard blood-based personal blood glucose measurement system.

• Journal of Sensor Science and Technology
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• v.15 no.5
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• pp.334-340
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• 2006

A compact biosignal monitoring device was developed. Electrodes for electrocardiogram (ECG) and a LED and silicon detector for photoplethysmogram (PPG) were used. A lead II type was arranged for ECG measurement and reflected light was measured at the finger tip for PPG. A single chip microprocessor (model ADuC812, Analog Device) controlled a measurement protocol and processed measured signals. PPG and ECG had a sampling rate of 300 Hz with 8-bit resolution. The maximum power consumption was 100 mW. The microprocessor computed pulse transit time (PTT) between the R-wave of ECG and the peak of PPG. To increase the resolution of PTT, analog peak detectors obtained the peaks of ECG and PPG whose interval was calculated using an internal clock cycle of 921.6 kHz. The device was designed to be operated by 3-volt battery. Biosignals can be measured for $2{\sim}3$ days continuously without the external interruptions and data is stored to an on-board memory. Our system was successfully tested with human subjects.

• Science of Emotion and Sensibility
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• v.14 no.1
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• pp.147-156
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• 2011

Basic emotions such as happiness, sadness, anger, fear, and disgust have been widely used to investigate emotion-specific autonomic nervous system activity in many studies. On the contrary, surprise emotion, Suggested also as one of the basic emotions suggested by Ekman et al. (1983), has been least investigated. The purpose of this study was to provide a description of cardiovascular responses on surprise stimulus using electrocardiograph (ECG) and photoplethysmograph (PPG). ECG and PPG were recorded from 76 undergraduate students, as they were exposed to a visuo-acoustic surprise stimulus. Heart rate (HR), standard deviation of R-R interval (SD-RR), root mean square of successive R-R interval difference (RMSSD-RR), respiratory sinus arrhythmia (RSA), finger blood volume pulse amplitude (FBVPA), and finger pulse transit time (FPTT) were calculated before and after the stimulus presentation. Results show significant increase in HR, SD-RR, and RMSSD-RR, decreased FBVPA, and shortened FPTT. Evidence suggests that surprise emotion can be characterized by vasoconstriction and accelerated heart rate, sympathetic activation, and increased heart rate variability, parasympathetic activation. These results can be useful in developing an emotion theory, or profiling surprise-specific physiological responses, as well as establishing the basis for emotion recognition system in human-computer interaction.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.2
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• pp.51-58
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• 2023

This paper proposes a deep learning method for estimating the heart rate from facial videos. Our proposed method estimates remote photoplethysmography (rPPG) signals to predict the heart rate. Although there have been proposed several methods for estimating rPPG signals, most previous methods can not be utilized in low-power single board computers due to their computational complexity. To address this problem, we construct a lightweight student model and employ a knowledge distillation technique to reduce the performance degradation of a deeper network model. The teacher model consists of 795k parameters, whereas the student model only contains 24k parameters, and therefore, the inference time was reduced with the factor of 10. By distilling the knowledge of the intermediate feature maps of the teacher model, we improved the accuracy of the student model for estimating the heart rate. Experiments were conducted on the UBFC-rPPG dataset to demonstrate the effectiveness of the proposed method. Moreover, we collected our own dataset to verify the accuracy and processing time of the proposed method on a real-world dataset. Experimental results on a NVIDIA Jetson Nano board demonstrate that our proposed method can infer the heart rate in real time with the mean absolute error of 2.5183 bpm.