• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.129-136
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• 2010

With mobile computing, wireless sensor network and sensor technologies, ubiquitous computing services are being realized and could satisfy the feasibility of ubiquitous healthcare to everyone. This u-Healthcare service can improve life quality of human since medical service can be provided to anyone, anytime, and anywhere. To confirm the vision of u-Healthcare service, we've implemented a healthcare system for heart disease patient which is composed of two components. Front-end collects various signals such as temperature, blood pressure, SpO2, and electrocardiogram, etc. As a backend, medical information server accumulates sensing data and performs back-end processing. To simply transfer these sensing values to a medical team may be too trivial. So, we've designed a model based on context awareness for more improved medical service which is based on artificial neural network. Through rigid experiments, we could confirm that the proposed system can provide improved medical service.

• Journal of IKEEE
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• v.22 no.4
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• pp.953-958
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• 2018

In this study, a portable detection system was developed that can detect harmful gas and signals simultaneously in an enclosed space of industrial sites and underground facilities. The developed system is a sensor module for gas detection, a patch type 1 channel small ECG sensor, a module for three-axial acceleration detection sensor, and a system for statistics. In order to verify the performance of the system modules, the digital resolution, signal frequency, output voltage, and ultra-small modules were evaluated. As a result of the performance of the developed system, the digital resolution was 300 (rps) and the signal amplification gain was 500 dB or more, and the ECG module was manufactured with $50mm{\times}10mm{\times}10mm$ to increase patch utilization. It is believed that the product of this research will be valuable if it is used as an IoT-based management system for real-time monitoring of industrial workers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2304-2320
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• 2021

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.29-36
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• 2021

In this paper, we classify ECG signal data for mobile devices using deep learning models. To classify abnormal heartbeats with high accuracy, three factors of the deep learning model are selected, and the classification accuracy is compared according to the changes in the conditions of the factors. We apply a CNN model that can self-extract features of ECG data and compare the performance of a total of 48 combinations by combining conditions of the depth of model, optimization method, and activation functions that compose the model. Deriving the combination of conditions with the highest accuracy, we obtained the highest classification accuracy of 97.88% when we applied 19 convolutional layers, an optimization method SGD, and an activation function Mish. In this experiment, we confirmed the suitability of feature extraction and abnormal beat detection of 1-channel ECG signals using CNN.

• Fashion & Textile Research Journal
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• v.24 no.6
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• pp.694-706
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• 2022

This study focusses on the development of a motion-sensing smart leggings prototype with the help of a module that monitors motion using a fiber-type stretch sensor. Additionally, it acquires data on Electrocardiogram (ECG), respiration, and body temperature signals, for the development of smart clothing used in online exercise coaching and customized healthcare systems. The research process was conducted in the following order: 1) Fabrication of a fiber-type elastic strain sensor for motion monitoring, 2) Positioning and attaching the sensor, 3) Pattern development and three-dimensional (3D) design, 4) Prototyping 5) Wearability test, and 6) Expert evaluation. The 3D design method was used to develop an aesthetic design, and for sensing accurate signal acquisition functions, wearability tests, and expert evaluation. As a result, first, the selection or manufacturing of an appropriate sensor for the function is of utmost importance. Second, the selection and attachment method of a location that can maximize the function of the sensor without interfering with any activity should be studied. Third, the signal line selection and connection method should be considered, and fourth, the aesthetic design should be reflected along with functional verification. In addition, the selection of an appropriate material is important, and tests for washability and durability must be made. This study presented a manufacturing method to improve the functionality and design of smart clothing, through the process of developing a prototype of motion-sensing smart leggings.

• The Journal of the Society of Stroke on Korean Medicine
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• v.6 no.1
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• pp.9-16
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• 2005

Objective : The heart rate variability is very useful indicator to study the function of the autonomic nervous system(ANS), and the physiologic signals can be observed based on the changes of the ANS of the heart. In order to assay the effects of the laser exposing to healthy subjects, the double blind test has been performed. Methods : This study included 62 healthy adults who have not any ANS disease and had normal sinus rhythm in electrocardiogram. The control group consisted of 31 subjects, laser group consisted of 31 subject. HRV was measured for 5 minutes before laser irradiation, sham and real laser irradiated for 30 minutes and than HRV remeasured for 5 minutes. Statistical significance was evaluated by independent T-test. Results : Mean HRV, Ln(VLF), Ln(HF), Ln(TP) of both groups at post-laser period decreased compared with that of the pre-laser period. Ln(LF) of both groups at post-laser period increased compared with that of the pre-laser period. LF/HF, SDNN of real laser group decreased and sham group decreased. Conclusions : There is no difference between two groups. The reason is presumed that all the studied subjects are healthy adults, and also the short and single transcutaneous laser irradiation would not influence upon changes of the ANS. The further study must be followed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.126-134
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• 2017

There have been numerous studies on extracting the R-peak from electrocardiogram (ECG) signals. However, most of the detection methods are complicated to implement in a real-time portable electrocardiograph device and have the disadvantage of requiring a large amount of calculations. R-peak detection requires pre-processing and post-processing related to baseline drift and the removal of noise from the commercial power supply for ECG data. An adaptive filter technique is widely used for R-peak detection, but the R-peak value cannot be detected when the input is lower than a threshold value. Moreover, there is a problem in detecting the P-peak and T-peak values due to the derivation of an erroneous threshold value as a result of noise. We propose a robust R-peak detection algorithm with low complexity and simple computation to solve these problems. The proposed scheme removes the baseline drift in ECG signals using an adaptive filter to solve the problems involved in threshold extraction. We also propose a technique to extract the appropriate threshold value automatically using the minimum and maximum values of the filtered ECG signal. To detect the R-peak from the ECG signal, we propose a threshold neighborhood search technique. Through experiments, we confirmed the improvement of the R-peak detection accuracy of the proposed method and achieved a detection speed that is suitable for a mobile system by reducing the amount of calculation. The experimental results show that the heart rate detection accuracy and sensitivity were very high (about 100%).

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.71-82
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• 2018

Stress involves changes in behavior, autonomic function and the secretion of hormones. Autonomic nervous system (ANS) contributes to physiological adaptive process in short durations. In particular, heart rate variability (HRV) analysis is commonly used as a quantitative marker depicting the ANS activity related to mental stress. The aim of this study is to investigate correlations between psychological responses to stress and HRV indices induced by the cognitive stressor. Thirty-three participants rated their mental and physical symptoms occurred during the past two weeks on Stress Response Inventory (SRI), which is composed of seven stress factors that may influence the status of mental stress levels. Then, they underwent the psychophysiological procedures, which are collected electrocardiogram (ECG) signals during a cognitive stress task. HRV indices, the standard deviation of R-R interval (SDNN), root mean square of successive R-R interval difference (RMSSD) and low frequency (LF)/high frequency (HF) ratio were extracted from ECG signals. Physiological responses were calculated stress responses by subtracting mean of the baseline from the mean of recovery. Stress factors such as tension, aggression, depression, fatigue, and frustration were positively correlated to HRV indices. In particular, aggression had significant positive correlations to SDNN, RMSSD and LF/HF ratio. Increased aggressive responses to stress correlated with the increases of all HRV indices. This means the increased autonomic coactivation. Additionally, tension, depression, fatigue, and frustration were positively associated with RMSSD reflecting increases in parasympathetic activation. The autonomic coactivation may represent an integrated response to specific cognitive reactions such as the orienting response.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.581-587
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• 2004

Conventional power spectrum methods based on FFT, AR method are not appropriate for analyzing biomedical signals whose spectral characteristics change rapidly. On the other hand, time-frequency analysis has more desirable characteristics of a time-varying spectrum. In this study, we investigated the spectral components of heart rate variability(HRV) in time-frequency domain using time frequency analysis methods. In the various time-frequency kernels functions, we studied the suitable kernels for the analysis of HRV using synthetic HRV signals. First, we evaluated the time/frequency resolution and cross term reduction of various kernel functions. Then, from the instantaneous frequency, obtained from time-frequency distribution, the method extracting frequency components of HRV was proposed. Subjects were 17 healthy young men. A coin-stacking task was used to induce mental stress. For each subjects, the experiment time was 3 minutes. Electrocardiogram, measured during the experiment, was analyzed after converted to HRV signal. In the results, emotional stress of subjects produced an increase in sympathetic activity. Sympathetic activation was responsible for the significant increase in the LF/HF ratio. Subjects were divided into two groups with task ability. Subjects who have higher mental stress have lack of task ability.

• Science of Emotion and Sensibility
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• v.22 no.1
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• pp.55-64
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• 2019

Stress is accompanied by changes in the responses of the autonomic nervous system, and the heart rate variability (HRV) index is a quantitative marker that reflects autonomic responses induced by stressors. In this study, we observed changes in the autonomic responses induced by combinations of 30% oxygen administration and color light for stress relaxation. In all, 42 participants produced stress symptoms over the preceding two weeks, as rated on the stress response scale. After stress assessment, they were exposed to three therapeutic conditions, and electrocardiogram (ECG) signals were recorded before, during, and after therapy. The three therapy conditions consisted of only 30% oxygen administration with white light, a combination of 30% oxygen and orange light, and a combination of 30% oxygen and blue light. The HRV indices extracted from ECG signals were heart rate (HR), the standard deviation of the RR interval (SDNN), the mean square root of consecutive RR interval difference values (RMSSD), the low frequency component of HRV (LF), the high frequency component (HF), and the LF/HF ratio. These indicators were used to compare mean values before and after therapy. The results showed that HR and the LF/HF ratio were significantly lower after therapy than before it. In particular, the condition with 30% oxygen and blue light yielded significantly greater RMSSD and HF increases, as well as decreases in LF/HF ratio than in other two conditions. Our results suggest that therapy with 30% oxygen and blue light is the most effective for the relaxation of stress, which implies autonomic balance by parasympathetic activation.