• Journal of Biomedical Engineering Research
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• v.25 no.2
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• pp.129-135
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• 2004

In the home health management system, we often face the situation to handle biological signals that are frequently measured from normal subjects. In such a case, it is necessary to decide whether the signal at a certain moment is normal or abnormal. Since ECC is one of the most frequently measured biological signals, we describe algorithms that detect QRS-complex and decide whether it is normal or abnormal. The developed QRS detection algorithm is a simplified version of the conventional algorithm providing enough performance for the proposed application. The developed classification algorithm that detects abnormal from mostly normal beats is based on QRS width, R-R interval and QRS shape parameter using Karhunen-Loeve transformation. The simplified QRS detector correctly detected about 99% of all beats in the MTT/BIH ECG database. The classification algorithm correctly classified about 96% of beats as normal or abnormal. The QRS detection and classification algorithm described in this paper could be used in home health management system.

• 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.

• Journal of the Ergonomics Society of Korea
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• v.30 no.6
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• pp.705-713
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• 2011

Objective: The aim of this study is to compare results of emotion recognition by several algorithms which classify three different emotional states(happiness, neutral, and surprise) using physiological features. Background: Recent emotion recognition studies have tried to detect human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 217 students participated in this experiment. While three kinds of emotional stimuli were presented to participants, ANS responses(EDA, SKT, ECG, RESP, and PPG) as physiological signals were measured in twice first one for 60 seconds as the baseline and 60 to 90 seconds during emotional states. The obtained signals from the session of the baseline and of the emotional states were equally analyzed for 30 seconds. Participants rated their own feelings to emotional stimuli on emotional assessment scale after presentation of emotional stimuli. The emotion classification was analyzed by Linear Discriminant Analysis(LDA, SPSS 15.0), Support Vector Machine (SVM), and Multilayer perceptron(MLP) using difference value which subtracts baseline from emotional state. Results: The emotional stimuli had 96% validity and 5.8 point efficiency on average. There were significant differences of ANS responses among three emotions by statistical analysis. The result of LDA showed that an accuracy of classification in three different emotions was 83.4%. And an accuracy of three emotions classification by SVM was 75.5% and 55.6% by MLP. Conclusion: This study confirmed that the three emotions can be better classified by LDA using various physiological features than SVM and MLP. Further study may need to get this result to get more stability and reliability, as comparing with the accuracy of emotions classification by using other algorithms. Application: This could help get better chances to recognize various human emotions by using physiological signals as well as be applied on human-computer interaction system for recognizing human emotions.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.378-382
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• 2006

This paper presents an approach to detect premature ventricular contractions(PVC) using the neural network with weighted fuzzy membership functions(NEWFM). NEWFM classifies normal and PVC beats by the trained weighted fuzzy membership functions using wavelet transformed coefficients extracted from the MIT-BIH PVC database. The two most important coefficients are selected by the non-overlap area distribution measurement method to minimize the classification rules that show PVC classification rate of 99.90%. By Presenting locations of the extracted two coefficients based on the R wave location, it is shown that PVC can be detected using only information of the two portions.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2003.05a
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• pp.148-153
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• 2003

본 논문에서는 심근허혈 질환을 효율적으로 분류하기 위한 신경망을 설계하였다. European ST-T DB의 심전도로부터 ST 분절의 특징을 추출하여 입력노드를 결정하고 10개의 학습률과 학습 횟수에 따른 신경망의 MES를 계산하였다. 실험 결과 특징 파라미터의 조합을 ST0, ST80, Slope, Area로 하였을 때 MSE를 가장 작았다. 이러한 특징 파라미터를 이용하여 신경망의 입력으로 학습시킨 경우 학습 횟수의 증가에 따라 MSE가 지수합수적으로 감소하였으며 1,000회 이상에서는 둔하게 감소하였다. 또한 학습 횟수가 5,000회, 10,000회, 15,000회 각각의 경우에 대하여 학습률을 0.01부터 0.7까지 증가시키면서 MSE를 계산한 결과 학습 횟수가 증가할수록 MSE를 최소로 하는 최적학습률이 0.1부터 0.04까지 감소하였다.

• Journal of Biomedical Engineering Research
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• v.39 no.2
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• pp.69-79
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• 2018

The present study proposes an algorithm that can discriminate between normal subjects and paroxysmal atrial fibrillation (PAF) patients, which is conducted using electrocardiogram (ECG) without PAF events. For this, time-domain features and random forest classifier are used. Time-domain features are obtained from Poincare plot, Lorenz plot of ${\delta}RR$ interval, and morphology analysis. Afterward, three features are selected in total through feature selection. PAF patients and normal subjects are classified using random forest. The classification result showed that sensitivity and specificity were 81.82% and 95.24% respectively, the positive predictive value and negative predictive value were 96.43% and 76.92% respectively, and accuracy was 87.04%. The proposed algorithm had an advantage in terms of the computation requirement compared to existing algorithm, so it has suggested applicability in the more efficient prediction of PAF.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2537-2539
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• 2004

SOM은 심전도 신호의 진단에 있어서 효과적인 Clustering을 해주는 신경망이라는 것을 몇몇의 실험을 통하여 알 수 있었다. [1] 하지만 출력노드의 크기를 임의로 지정해야 하는 문제점이 있고 일반적으로 출력층의 크기가 클수록 진단결과는 좋지만 인간시간은 오래걸린다는 단점이 있다. 따라서 진단능력과 학습속도 사이의 균형에 관련된 문제가 대두되게 된다. 본 논문에서는 이러한 문제점을 극복하고자 기존의 SOM 신경망의 단점을 보완하고자 GCS(Growing Cell Structures)를 이용한 심전도의 학습속도와 분류능력 사이의 효율성 개선 방안을 제안한다. 이 방범은 GCS를 이용하여 적절한 노드의 수를 찾아내는 것이다. 이를 이용한 심전도 진단의 실험을 통해 기존의 SOM이 할 수 없었던 자체적인 출력노드의 증감을 행함을 확인할 수 있었다. 또한 출력노드의 감소로 인해 연산량이 줄어 학습시간의 효율성이 증가하였다.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.935-939
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• 2003

The emotion recognition is one of the most significant interface technologies which make the high level of human-machine communication possible. The central nervous system stimulated by emotional stimuli affects the autonomous nervous system like a heart, blood vessel, endocrine organs, and so on. Therefore bio-signals like HRV, ECG and EEG can reflect one' emotional state. This study investigates the correlation between emotional states and bio-signals to realize the emotion recognition. This study also covers classification of human emotional states, selection of the effective bio-signal and signal processing. The experimental results presented in this paper show possibility of the emotion recognition.

• Journal of Biomedical Engineering Research
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• v.17 no.2
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• pp.263-273
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• 1996

The time instants, at which QRS complexes are detected, are used in the electrocardioyam rhythm analysis. Hence, it is necessary that all QRS complexes are detected and that no other waves or artifacts are wrongly labeled as such. These time instants are also used in other tasks as an indication of the location of significant events in the ECG. For example, the QRS typification algorithm uses these points to define the region of interest for complex comparison and alignment. When waveform recognition is drone for each complex, these points are used to define search intervals in which the onset and the end of the QRS nmplex have to be found This paper proposes the method for the detection of QRS complexes and decision rule for the classification scheme. The efficiency of the detection is demonstrated with the aid of an internationally validated CSE(Common Standard for Quantitative Electrocardioyaph) data set 3 and 4.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.443-446
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• 1997

HRV(heart rate variability) is the time series data of R-R interval time duration based on ECGs. Power spectral analysis of HRV has recently been used to define the activity of ANS(autonomic nervous system). In this study, 14 rats were divided into two groups, sympathectomy and vagotomy. During the experiments, ECGs of rats were collected three times at each experimental conditions or the duration of 5 minutes, where sampling frequency was set at 2KHz. After the application of the Berger's Serires algorithm to ECG raw data, power spectrum of HRV was obtained via FFT. Results showed that HF/LF were increased or the sympathectomy group and decreased or the vagotomy group. It implies that the variations in HF/LF components could be used or the ANS function classification.