• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.945-952
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• 2013

The R wave of QRS complex is the most prominent feature in ECG because of its specific shape; therefore it is taken as a reference in ECG feature extraction. But R wave detection suffers from the fact that frequency bands of the noise/other components such as P/T waves overlap with that of QRS complex. ECG signal processing must consider efficiency for hardware and software resources available in processing for miniaturization and low power. In other words, the design of algorithm that exactly detects QRS region using minimal computation by analyzing the person's physical condition and/or environment is needed. Therefore, efficient QRS detection based on SOM(Subtractive Operation Method) is presented in this paper. For this purpose, we detected R wave through the preprocessing method using morphological filter, empirical threshold, and subtractive signal. Also, we applied dynamic backward searching method for efficient detection. The performance of R wave detection is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.41% in R wave detection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.8
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• pp.1947-1954
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• 2013

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. But it is difficult to detect the P and T wave signal because of person's individual difference. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extrating minimal feature. In this paper, we propose arrhythmia detection based on binary coding using QRS feature varibility. For this purpose, we detected R wave, RR interval, QRS width from noise-free ECG signal through the preprocessing method. Also, we classified arrhythmia in realtime by converting threshold variability of feature to binary code. PVC, PAC, Normal, BBB, Paced beat classification is evaluated by using 39 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 97.18%, 94.14%, 99.83%, 92.77%, 97.48% in PVC, PAC, Normal, BBB, Paced beat classification.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.305-309
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• 2002

본 연구에서는 조도조건에 따른 VDT작업자의 각성도를 평가하기 위하여 10 lx와 1500 lx 2종류의 조도 조건으로 하여 모니터 상에서 1자리 숫자 3개를 더하는 단조연산작업을 수행하였다. 그 결과 155 lx보다 비교적 졸음이 쉽게 유발되며 주의집중정도가 낮은 조도 10 lx에서의 정답률이 더 낮게 나왔다. 또한, 두 조도조건 하에서 호흡간격은 작업수행시 큰 차이가 안 나타난 반면 RR간격은 10 lx에서는 작업수행 횟수가 증가함에 따라 점차 증가하지만 1500 lx에서는 거의 변화가 없었다.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.671-681
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• 2019

Studies on predicting arrhythmia using machine learning have been actively conducted with increasing number of arrhythmia patients. Existing studies have predicted arrhythmia based on multivariate data of feature variables extracted from RR interval data at a specific time point. In this study, we consider that the pattern of the heart state changes with time can be important information for the arrhythmia prediction. Therefore, we investigate the usefulness of predicting the arrhythmia with multivariate time series data obtained by extracting and accumulating the multivariate vectors of the feature variables at various time points. When considering 1-nearest neighbor classification method and its ensemble for comparison, it is confirmed that the multivariate time series data based method can have better classification performance than the multivariate data based method if we select an appropriate time series distance function.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.450-460
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• 2017

T wave is cardiac parameters that represent ventricular repolarization, it is very important to diagnose arrhythmia. Several methods for detecting T wave have been proposed, such as frequency analysis and non-linear approach. However, detection accuracy is at the lower level. This is because of the overlap of the P wave and T wave depending on the heart condition. We propose T wave detection algorithm based on target area extraction through QRS cancellation and moving average. For this purpose, we detected Q, R, S wave from noise-free ECG(electrocardiogram) signal through the preprocessing method. And then we extracted P, T target area by applying decision rule for four PAC(premature atrial contraction) pattern another arrhythmia through moving average and detected T wave using RT interval and threshold of RR interval. The performance of T wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. The achieved scores indicate the average detection rate of 95.32%.

• Journal of Biomedical Engineering Research
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• v.24 no.1
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• pp.1-8
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• 2003

The job of long distance driving is likely to be fatiguing and requires long period alertness and attention, which make considerable demands of the driver. Driving fatigue contributes to driver related with accidents and fatalities. In this study, we investigated the relationship between the number of hours of driving and driving fatigue using heart rate variability(HRV) signal. With a more traditional measure of overall variability (standard deviation, mean, spectral values of heart rate). Nonlinear characteristics of HRV signal were analyzed using Approximate Entropy (ApEn) and Poincare plot. Five subjects drive the four passenger vehicle twice. All experiment number was 40. The test route was about 300Km continuous long highway circuit and driving time was about 3 hours. During the driving, measures of electrocardiogram(ECG) were performed at intervals of 30min. HRV signal, derived from the ECG, was analyzed using time, frequency domain parameters and nonlinear characteristic. The significance of differences on the response to driving fatigue was determined by Student's t-test. Differences were considered significant when a p value < 0.05 was observed. In the results, mean heart rate(HRmean) decreased consistently with driving time, standard deviation of RR intervals(SDRR), standard deviation of the successive difference of the RR intervals(SDSD) increased until 90min. Hereafter, they were almost unchanging until the end of the test. Normalized low frequency component $(LF_{norm})$, ratio of low to high frequency component (LF/HF) increased. We used the Approximate Entropy(ApEn), Poincare plot method to describe the nonlinear characteristics of HRV signal. Nonlinear characteristics of HRV signals decreased with driving time. Statistical significant is appeared after 60 min in all parameters.

• Science of Emotion and Sensibility
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• v.20 no.3
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• pp.169-180
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• 2017

Individuals with high trait anxiety try to suppress their anger expression, thus there are limits in measuring their anger using subjective behavioral evaluation. In order to overcome this limitation, this study attempted to identify the difference in the autonomic nervous system responses induced by anger in individuals with high trait anxiety. Participants were divided into two groups, anxiety and control groups. Electrocardiogram (ECG), respiration (RESP), electrodermal activity (EDA), and skin temperature (SKT) were measured while participants were presented with an anger-inducing stimulus. Heart rate (HR), standard deviation of NN interval (SDNN), root mean square of successive difference (RMSSD), low frequency (LF), high frequency (HF), LF/HF ratio, respiration rate (RR), skin conductance level (SCL), and maximum skin temperature (maxSKT) were calculated before and after presenting the stimulus. Anxiety group reported greater anger by the anger-inducing stimulus compared to the control group. Anxiety group also showed significant increase in SDNN and LF, and decrease in HF, LF/HF ratio, and RR. These results suggest that the autonomic nervous system responses may be used as objective indicators of anger experiences in individuals with high trait anxiety.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.80-80
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• 2013

대규모 광도곡선 자료에서 다양한 주기변광성들의 정확한 주기를 효율적으로 검출하는 실험을 시도하였다. 실험을 위해 OGLE-III 맥동 변광성(RR Lyrae, Delta Scuti, Cepheid) 목록 중, I 필터로 관측된 총 31,324개의 광도 곡선을 사용하였다. 이 실험에 사용한 주기분석 알고리즘 MS_Period(Multi-Step period searching method)는 주기를 놓치지 않기 위해 두 가지 다른 방법(Multi Polynomial function, Phase Dispersion)으로 후보 주기를 구하고 정밀주기를 도출하기 위해 후보 주기 주변부를 Spline fitting을 통해 재탐색하는 방법이다. 기존의 MS_Period 방식은 주기 탐색 간격(dP/P)이 일정하였으나, 우리는 탐색 주기 구간을 나누고 짧은 주기에서는 작은 간격으로, 긴 주기에서는 보다 넓은 간격으로 주기를 탐색하는 과정을 추가하였다. 그 결과 98% 이상의 별에서 OGLE-III와 거의 일치하는 주기를 얻었으며, 긴 주기에서의 불필요한 정밀 탐색을 회피함으로써 분석시간도 단축되었다. 주기 결정이 어려운 경우들은 주로 1) periodogram에서 실제 주기가 아닌 1일 근처에서 noise보다 큰 peak가 보이는 경우, 2) 하나의 별에 대해 여러 주기가 비슷한 Phase diagram을 보이고, periodogram에서도 비슷한 peak를 갖는 경우, 3) OGLE-III의 주기와 전혀 다른 주기만 찾은 경우, 4) OGLE-III에서 제시하지 않은 혼합된 주기의 존재가 의심되는 경우인 것을 확인하였고, 각 사례들의 특징을 살펴보았다.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.271-280
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• 2011

Heart Rate Variability (HRV) analysis is a convenient tool to assess Myocardial Ischemia (MI). The analysis methods of HRV can be divided into time domain and frequency domain analysis. This paper uses wavelet transform as frequency domain analysis in contrast to time domain analysis in short term HRV analysis. ST-T and normal episodes are collected from the European ST-T database and the MIT-BIH Normal Sinus Rhythm database, respectively. An episode can be divided into several segments, each of which is formed by 32 successive RR intervals. Eighteen HRV features are extracted from each segment by the time and frequency domain analysis. To diagnose MI, the Neural Network with Weighted Fuzzy Membership functions (NEWFM) is used with the extracted 18 features. The results show that the average accuracy from time and frequency domain features is 75.29% and 80.93%, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.2021-2030
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• 2012

Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Particularly, in the healthcare system that must continuously monitor patient's situation, it is necessary to process ECG (Electrocardiography) signal in realtime. In other words, the design of algorithm that exactly detects R wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, the patient adaptive pattern matching algorithm for the classification of PVC is presented in this paper. For this purpose, we detected R wave through the preprocessing method, adaptive threshold and window. Also, we applied pattern matching method to classify each patient's normal cardiac behavior through the Hash function. The performance of R wave detection and abnormal beat classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.33% in R wave detection and the rate of 0.32% in abnormal beat classification error.