• Journal of Biomedical Engineering Research
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• v.35 no.3
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• pp.42-49
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• 2014

We propose an individual identification method using a single-lead electrocardiogram signal. In this paper, lead I ECG is measured from subjects in various physical and psychological states. We performed a noise reduction for lead I signal as a preprocessing stage and this signal is used to acquire the representative beat waveform for individuals by utilizing the ensemble average. From the P-QRS-T waves, features are extracted to identify individuals, 19 using the duration and amplitude information, and 16 from the QRS complex acquired by applying Pan-Tompkins algorithm to the ensemble averaged waveform. To analyze the effect of each feature and to improve efficiency while maintaining the performance, Relief-F algorithm is used to select features from the 35 features extracted. Some or all of these 35 features were used in the support vector machine (SVM) learning and tests. The classification accuracy using the entire feature set was 98.34%. Experimental results show that it is possible to identify a person by features extracted from limb lead I signal only.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.887-889
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• 2016

본 연구는 전도성 섬유를 양말에 부착하여 발바닥에서 심전도(ECG, Electrocardiogram) 신호를 검출하였다. 발바닥에서 측정한 심전도 신호와 손목에서 측정한 심전도 신호에 Pan-Tompkins algorithm을 적용하였고 R-R interval을 검출하였다. 이후 발바닥과 손목에서 측정된 심전도의 유의성을 검출하기 위해 비모수 검정법인 Spearman검정을 사용하여 상관분석을 수행하였다. 상관분석 결과, 유의확률 p=0.00에서 correlation coefficient=0.901로 두 데이터는 강한 양의 선형 관계에 있는 것으로 나타났다.

• Journal of the Korea Society of Computer and Information
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• v.22 no.5
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• pp.65-72
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• 2017

Premature Ventricular Contraction(PVC) arrhythmia is most common abnormal-heart rhythm that may increase mortal risk of a cardiac patient. Thus, it is very important issue to identify the specular portraits of PVC pattern especially from the patient. In this paper, we propose a new method to extract the characteristics of PVC pattern by applying K-means machine learning algorithm on Heart Rate Variability depicted in Poinecare plot. For the quantitative analysis to distinguish the trend of cluster patterns between normal sinus rhythm and PVC beat, the Euclidean distance measure was sought between the clusters. Experimental simulations on MIT-BIH arrhythmia database draw the fact that the distance measure on the cluster is valid for differentiating the pattern-traits of PVC beats. Therefore, we proposed a method that can offer the simple remedy to identify the attributes of PVC beats in terms of K-means clusters especially in the long-period Electrocardiogram(ECG).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1728-1736
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• 2015

Several algorithms have been developed to classify arrhythmia which either rely on specific ECG(Electrocardiogram) database. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. 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 extracting minimal feature. In this paper, we propose arrhythmia classification method using QRS Pattern of ECG signal according to personalized type. For this purpose, we detected R wave through the preprocessing method and define QRS pattern of ECG signal by QRS feature Also, we detect and modify by pattern classification, classified arrhythmia duplicated QRS pattern in realtime. Normal, PVC, PAC, LBBB, RBBB, Paced beat classification is evaluated by using 43 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.98%, 97.22%, 95.14%, 91.47%, 94.85%, 97.48% in PVC, PAC, Normal, BBB, Paced beat classification.

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.10
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• pp.1665-1672
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• 2009

This study examines the comfort of outdoorwear by electrocardiogram (ECG) and electroencephalogram (EEG) analyses. An experiment that consisted of rest (30 min), exercise (30 min), and recovery (20 min) periods was administered in a climate chamber with 10 healthy male participants. Two kinds of outdoorwear made of 100% cotton fabrics ('Control') and specially engineered fabrics having the feature of quick sweat absorbency and high speed drying fabric ('Functional') are evaluated in the experiment. ECG and EEG signals were obtained during the rest and recovery periods for the two outdoorwear conditions. The ECG analysis identified a smaller decrement of high frequency (HF) power for the 'Functional' when compared with the 'Control' during the recovery period. Next, the EEG analysis showed that the relative band powers of slow $\alpha$ and mid $\alpha$ increased for the 'Functional' while they decreased for the 'Control' and that the ratio of $\alpha$ power to high $\beta$ power was higher for the 'Functional'. The evaluation results indicate that the participants could remain relaxed more with less stress while wearing the functional outdoorwear that demonstrated the positive effects on autonomic nervous system (ANS) activities. The present study is significant in regard that use of ECG and EEG for the assessment of wear comfort is the first in the field of clothing and textile.

• Smart Media Journal
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• v.8 no.4
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• pp.33-37
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• 2019

Because the ECG signals are time-series data acquired as time elapses, it is important to obtain comparative data the same in size as the enrolled data every time. This paper suggests a network model of GAN (Generative Adversarial Networks) based on an auxiliary classifier to generate synthetic ECG signals which may address the different data size issues. The Cosine similarity and Cross-correlation are used to examine the similarity of synthetic ECG signals. The analysis shows that the Average Cosine similarity was 0.991 and the Average Euclidean distance similarity based on cross-correlation was 0.25: such results indicate that data size difference issue can be resolved while the generated synthetic ECG signals, similar to real ECG signals, can create synthetic data even when the registered data are not the same as the comparative data in size.

• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.280-289
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• 2022

In this paper, we present a QRS-complex detection algorithm to calculate an accurate heartbeat and clearly recognize irregular rhythm from ECG signals. The conventional Pan-Tompkins algorithm brings false QRS detection in the derivative when QRS and noise signals have similar instant variation. The proposed algorithm uses amplitude differences in 7 adjacent samples to detect QRS-complex which has the highest amplitude variation. The calculated amplitude is cubed to dominate QRS-complex and the moving average method is applied to diminish the noise signal's amplitude. Finally, a decision rule with a threshold value is applied to detect accurate QRS-complex. The calculated signals with Pan-Tompkins and proposed algorithms were compared by signal-to-noise ratio to evaluate the noise reduction degree. QRS-complex detection performance was confirmed by sensitivity and the positive predictive value(PPV). Normal ECG, muscle noise ECG, PVC, and atrial fibrillation signals were achieved which were measured from an ECG simulator. The signal-to-noise ratio difference between Pan-Tompkins and the proposed algorithm were 8.1, 8.5, 9.6, and 4.7, respectively. All ratio of the proposed algorithm is higher than the Pan-Tompkins values. It indicates that the proposed algorithm is more robust to noise than the Pan-Tompkins algorithm. The Pan-Tompkins algorithm and the proposed algorithm showed similar sensitivity and PPV at most waveforms. However, with a noisy atrial fibrillation signal, the PPV for QRS-complex has different values, 42% for the Pan-Tompkins algorithm and 100% for the proposed algorithm. It means that the proposed algorithm has superiority for QRS-complex detection in a noisy environment.

• Fibers and Polymers
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• v.7 no.4
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• pp.446-449
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• 2006

The purpose of this study was to evaluate the comfort of PET clothing treated by UV. The physiological responses of the human body were investigated. Mean skin temperature and physiological signals such as Electroencephalogram (EEG), and heart rate (Electrocardiogram, (ECG)) were examined for 20 minutes during stable wearing conditions. Mean skin temperature was measured every two seconds using Ramanathan's method. Physiological responses were measured using Biopac MP100 series and analyzed using the software, Acqknowledge 3.5.2. Psychological effects were analyzed every five minutes. Comfort of untreated PET clothing decreased with the passage of time. Compared with PET clothing untreated, treated for 30 minutes, and treated for 90 minutes, the analysis of EEG showed that PET clothing treated for 90 minutes was the most comfortable after 20 minutes. In addition, the interval of the heart rate shown on the ECG was the highest in PET clothing treated for 90 minutes. Skin temperature was the lowest in PET treated for 90 minutes. We thus conclude that suitable UV irradiation would improve comfort.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.624-627
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• 2008

본 연구에서는 유비쿼터스 헬스케어를 위하여 비침습적으로 측정기 가능하고, 많은 건강정보를 포함하고 있는 심전도(electrocardiogram, ECG)와 광전용적맥파(photo plethysmograph, PPG)를 측정하고자 하였다. 이를 위하여 배터리로 구동 가능한 초소형의 심전도 및 맥파측정 시스템의 구현을 위하여 각각의 신호를 검출 및 신호처리하기 위한 회로를 구현하였다. 그리고 계측된 심전도 및 맥파신호의 무선전송을 위하여 초저전력 무선센서네트워크 기술을 적용한 무선 생체신호 전송시스템을 구현하였다. 계측된 심전도의 R파 정점과 인체의 말초부위에서 측정한 맥파의 기준점 사이의 시간인 맥파전달시간(pulse transit time, PTT)을 분석하여 심장에서 말초부위까지 혈관의 물리적 특성을 평가함으로써 동맥경화와 같은 혈관질환의 사전모니터 링이 가능한 시스템을 구현하고자 하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.358-359
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• 2012

본 연구에서는 범용적인 건강 모니터링에 활용할 수 있는 생체신호인 심전도를 일반 가정 내에서 뿐만 아니라 일상생활 중에서도 실시간으로 편리하게 측정할 수 있도록 초소형 저전력의 착용형 심전도 계측시스템을 구현하였다. 이를 위하여 표준 12-lead법이 아닌 모바일 또는 휴대용 장치에 적합한 2-lead법을 사용하여 심전도 계측부를 구현하였고, 심전도 계측부를 베이스 노드로 하여 심전도 신호를 가정 내 또는 실외에서도 무선으로 전송 할 수 있도록 구현하였다. 먼저 가정 내에서는 저 전력 무선센서노드를 이용하여 심전도 신호를 실시간으로 PC에 전송하여 모니터링이 가능하도록 구현 하였고, 실외에서는 저전력 통신 방식인 Bluetooth 2.0을 사용하여 스마트폰으로 심전도 신호를 실시간으로 전송해 모니터링 할 수 있도록 구현하였다.