• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.414-421
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• 2015

This paper presents a new method to decompose a duplicated ECG signal, which is measured from two people, to two individual ECG signals. In paper, it is shown that the duplicated ECG signal can be decomposed, provided that their SAECG signals are known. As the SAECG signal is the average of a ECG signal, it is a feature to identify individual ECG signals from the duplicated signal. Since the ECG signal is nearly periodic, so-called heart-rate, the period of each ECG signal can be found by using the autocorrelation of the duplicated signal, That is, the autocorrelation has high peaks at the multiple instants of heart-rate of each person. With the heart-rate of each person obtained by some processing, all R-peaks are identified by the SAECG signals. To be concrete, the SAECG signal of each person is repeatedly placed at the R-peak instants with his heart-rate, and the weight of each SAECG signal is computed by LMSE optimization. Finally, as adding the error signal in the LMSE optimization processing to the weighted SAECG signal, each individual ECG signal is obtained. In experimental results, we demonstrate that the duplicated ECG signal is successfully decomposed into two ECG signals.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.277-283
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• 2023

Vital signals provide essential information regarding the health status of individuals, thereby contributing to health management and medical research. Present monitoring methods, such as ECGs (Electrocardiograms) and smartwatches, demand proximity and fixed postures, which limit their applicability. To address this, Non-contact vital signal measurement methods, such as CW (Continuous-Wave) radar, have emerged as a solution. However, unwanted signal components and a stepwise processing approach lead to errors and limitations in heart rate detection. To overcome these issues, this study introduces an integrated neural network approach that combines noise removal, demodulation, and dominant-frequency detection into a unified process. The neural network employed for signal processing in this research adopts a MLP (Multi-Layer Perceptron) architecture, which analyzes the in-phase and quadrature signals collected within a specified time window, using two distinct input layers. The training of the neural network utilizes CW radar signals and reference heart rates obtained from the ECG. In the experimental evaluation, networks trained on different datasets were compared, and their performance was assessed based on loss and frequency accuracy. The proposed methodology exhibits substantial potential for achieving precise vital signals through non-contact measurements, effectively mitigating the limitations of existing methodologies.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1978_1979
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• 2009

Heart rate may be a very important parameter in human health. To extract heart rate, the electrocardiogram(ECG) is commonly used. But the ECG acquisition procedure is somewhat complex. On the other hand, the acquisition of pulse wave or photoplethysmogram(PPG) is very easy. However, the peak of PPG is not so sharp as ECG. This study tries to enhance the performance of period detection in PPG signal. The method uses the average slopes around the main peak. The crossing point of the increasing and the decreasing slopes is selected as the peak point of heart rate period. The proposed method showed smoothed heart rate graph and reduced irregularity in heart rate values.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.234-243
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• 2017

With the development of smartphone technologies enable photoplethysmogram (PPG) acquisition by camera and heart rate (HR) measurement. This papers presents improved algorithm to extract HR from PPG signal recorded by smartphone camera and to develop real-time PPG signal processing Android application. 400 video samples recorded by Samsung smartphone camera are imported as input data for further processing and evaluating algorithm on MATLAB. An optimized algorithm is developed and tested on Android platform with different kind of Samsung smartphones. To assess algorithm's performance, medical device Beurer BC08 is used as reference. According to related works, accuracy parameters includes 90% number of samples that has relative errors less than 5%, Person correlation (r) more than 0.9, and standard estimated error (SEE) less than 5 beats-per-minutes (bpm).

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.9
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• pp.547-553
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• 2003

In this paper, we designed the PC-based analyzer of the power spectral density that could estimate the heart rate variability from time series data of R-R interval. The power spectral density estimated that it applied the autoregressive model to the measured electrocardiogram during a short period. Also, the characteristics of the designed analyzer are that it could process of the signal filtering, the generation and recomposition of time series and the feature extraction at the same time. Especially the analyzer reconstructed which applied the lowpass filter of the time series composed by the linear interpolation so as to enhance the signal-to-noise feature. We could estimate the power spectral density that confirmed a variety of power peak with low frequency range and high frequency rang of autonomic nerve by the heart rate variability.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.3
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• pp.71-78
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• 2009

In this study, estimation of heart rate from measured PPG signal is proposed. PPG signal is to be measured blood flow in a blood vessel effected by systole and diastole. PPG sianl has single frequency so that PPG frequency can be tracked by 2nd IIR adaptive notch filter. PPG frequency is obtained continually from updating filter coefficient throughout adaptive algorithm and then the heart rate of human is approximately estimated.

• Proceedings of the Korea Society for Simulation Conference
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• 2004.05a
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• pp.109-117
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• 2004

In this paper, we consider the aortic sinus baroreceptor, which is the most representative baroreceptor sensing the variance of pressure in the cardiovascular system, and propose heart activity control model to observe the effect of delay time in heart period and stroke volume under the regulation of baroreflex in the aortic sinus. The proposed heart activity baroreflex regulation model contains electric circuit sub-model. We constituted the time delay sub-model to observe sensitivity of heart activity baroreflex regulation model by using the variable value to represent the control signal transmission time from the output of baroreflex regulation model to efferent nerve through central nervous system. The simulation object of this model is to observe variability of the cardiovascular system by variable value in time delay sub-model. As simulation results, we observe three patterns of the cardiovascular system variability by the time delay, First, if the time delay over 2.5 second, aortic pressure and stroke volume and heart rate is observed nonperiodically and observed. Finally, if time delay under 0.1 second, then heart rate and aortic pressure-heart rate trajectory is maintained in stable state.

• Journal of Multimedia Information System
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• v.8 no.1
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• pp.1-10
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• 2021

Heart Rate is a crucial physiological parameter that provides basic information about the state of the human body in the cardiovascular system, as well as in medical diagnostics and fitness assessments. At present day, it has been demonstrated that facial video-based photoplethysmographic signal captured using a low-cost RGB camera is possible to retrieve remote heart rate. Traditional heart rate measurement is mostly obtained by direct contact with the human body, therefore, it can result inconvenient for long-term measurement due to the discomfort that it causes to the subject. In this paper, we propose a non-contact-based remote heart rate measuring approach of the subject which depends on the color intensity variation of the subject's facial skin. The proposed method is applied in two regions of the subject's face, forehead and cheeks. For this, three different algorithms are used to measure the heart rate. i.e., Fast Fourier Transform (FFT), Independent Component Analysis (ICA) and Principal Component Analysis (PCA). The average accuracy for the three algorithms utilizing the proposed method was 89.25% in both regions. It is also noteworthy that the FastICA algorithm showed a higher average accuracy of more than 92% in both regions. The proposed method obtained 1.94% higher average accuracy than the traditional method based on average color value.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.222-227
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• 2022

The purpose of this study was to investigate changes in heart rate according to recovery methods after circuit weight training exercise. Fourteen men in their twenties were selected as subjects, and three sets of circuit weight training were performed by cycling six sports, and two recovery conditions (dynamic and static) were performed immediately after exercise. Changes in heart rate did not have an interactive effect according to recovery method and time, and both conditions showed significant changes between sets 1 and 2, and between sets 3 and after recovery. In this study, the high heart rate of 2 sets and 3 sets was seen as a result of exercise stimulation, and the low heart rate of 1 set was thought to be due to the decrease in vagus nerve activity rather than the role of catecholamines. On the other hand, the heart rate after 20 minutes of exercise did not show any difference according to the recovery method, which could mean that the recovery process due to the aquatic environment can act more strongly than the process of dynamic recovery and static recovery. It is thought that the characteristics affected the sensory and circulation of the body, and thus the change of the afferent signal and the level of metabolic products generated in the active muscle.

• International Journal of Vascular Biomedical Engineering
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• v.3 no.1
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• pp.14-22
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• 2005

In the present study, our aim is to investigate whether responses to the head-up tilt (HUT) on nonlinear properties of heart rate variability (HRV) in young and elderly subjects are different or not. Thirteen young-healthy subjects ($24.5{\pm}3.7$ years) and 18 old-aged healthy subjects ($74.5{\pm}7.4$ years) participated in this study. An electrocardiogram (ECG) in the supine posture, at $0^{\circ}$, and in the standing posture, at $70^{\circ}$ of head-up tilt, was recorded. Detrended fluctuation analysis (DFA) and approximate entropy (ApEn), measures of short-/long-term correlation properties and overall complexity of heart rate (HR) respectively, along with spectral components of HR variability (HRV) were analyzed for both the supine and HUT postures. We observed that the short-term fractal exponent ${\alpha}_1$ increased during HUT posture (F(1, 29) = 39.79, P = 0.000), especially, the young subjects showed a significantly higher values compared to the elderly subjects. ApEn significantly decreased (F(1, 29) = 8.61, P = 0.006) during HUT posture. HUT posture decreased the complexity in HR dynamics and increased short-term fractal exponent values in young subjects but not in elderly subjects. These results imply that there are differences of response to HUT on nonlinear properties between young and elderly subjects.