• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.175-176
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• 1998

The automated ECG diagnostic systems that are used in hospitals have low performance of P wave detection when faced with some diseases such as conduction block. So, the aim of this study is the improvement of detection performance in conduction block which is low in P wave detection. Median QRS-T segments were subtracted from the raw data and residual in the QRS-T regions was zeroed. After band pass filtering, we applied approximated length transformation to detect P wave.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.453-456
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• 2005

일반적으로 심전도는 심장계통의 질환을 판단할 때 사용된다. 이러한 심장질환의 이상 유무를 자동으로 진단하기 위해서는 QRS파형 검출을 필요로 하며, 이를 위하여 웨이블렛변환 방법이나 템플릿매칭, 룰 베이스 방법 등 여러 가지 방법들이 쓰이고 있으나, 심전도 신호가 표준화된 형태를 갖지 않는 경우는 검출 능력에 많은 한계를 갖고 있다. 본 논문은 파형의 베이스라인(baseline)을 기준으로 진폭 값에 절대치을 취하는 방법으로 파형의 R피크값을 검출하는 알고리즘을 제안한다. 결과를 검증하기 위해 MIT-BIH 데이타베이스에서 제공하는 데이터와 R피크값을 본 논문의 알고리즘으로 추출된 R피크값과 비교한 결과 96.7%의 검출률을 보였다.

• The KIPS Transactions:PartD
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• v.19D no.1
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• pp.105-112
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• 2012

Most methods for detecting PVC and APC require the measurement of accurate QRS complex, P wave and T wave. In this study, we propose new algorithm for detecting PVC and APC without using complex parameter and algorithms. Proposed algorithm have wide applicability to abnormal waveform by personal distinction and difference as well as all sorts of normal waveform on ECG. To achieve this, we separate ECG signal into each unit patterns and made a standard unit pattern by just using unit patterns which have normal R-R internal. After that, we detect PVC and APC by using similarity analysis for pattern matching between standard unit pattern and each unit patterns.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.313-316
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• 2009

A novel approach for electrocardiogram (ECG) analysis within a functional sensor node has been developed and evaluated. The main aim is to reduce data collision, traffic over loads and power consumption in healthcare applications of wireless sensor networks (WSN). The sensor node attached on the patient's bodysurface around the heart can perform ECG analysis based on a QRS detection algorithm to detect abnormal condition of the patient. Data transfer is activated only after detected abnormality in the ECG. This system can reduce packet loss during transmission by reducing traffic overload. In addition, it saves power supply energy leading to more reliable, cheap and user-friendly operation in the WSN based ubiquitous health monitoring.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1490-1499
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• 2022

Arrhythmia is a condition in which the heart has an irregular rhythm or abnormal heart rate, early diagnosis and management is very important because it can cause stroke, cardiac arrest, or even death. In this paper, we propose arrhythmia classification using hybrid combination model of CNN-BLSTM. For this purpose, the QRS features are detected from noise removed signal through pre-processing and a single bit segment was extracted. In this case, the GAN oversampling technique is applied to solve the data imbalance problem. It consisted of CNN layers to extract the patterns of the arrhythmia precisely, used them as the input of the BLSTM. The weights were learned through deep learning and the learning model was evaluated by the validation data. To evaluate the performance of the proposed method, classification accuracy, precision, recall, and F1-score were compared by using the MIT-BIH arrhythmia database. The achieved scores indicate 99.30%, 98.70%, 97.50%, 98.06% in terms of the accuracy, precision, recall, F1 score, respectively.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1034-1042
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• 2016

The detection of P-waves and T-wave in the electrocardiogram signal analysis is an important issue. But the accuracy of the boundary detection algorithm is an insufficient level in the change of slow transition in the signal compared to the QRS complex. This study proposes an algorithm to detect P-wave and T-wave sequentially after determining local baseline using QRS complex. First, we detected the peak points based on local baseline and determined the onset and offset through the calculation of the area of the section. After modifying the baseline using detected waveform, we detected the other waveform in the same way and separated the P-wave and the T-wave based on the location. We used the PhysioNet QT database to evaluate the performances of the algorithm, and calculate the mean and the standard deviations. The experiment results show that standard deviations are under the tolerances accepted by expert physicians, and outperform the results obtained by the other algorithms.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.8
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• pp.39-46
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• 1998

One of the main techniques for diagnosing heart disease is by examining the electrocardiogram(ECG). Many studies on detecting the QRS complex, P, and T waves have been performed because meaningful information is contained in these parameters. However, the earlier detection techniques can not effectively extract those parameters from the ECG that is severely contaminated by noise source. In this paper, we performed the extracting parameters from and recovering the ECG signal using wavelets transform that has recently been applying to various fields.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.288-292
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• 1996

This paper describes far the detection of heart event that is, QRS complex and P wave which are result from heart activity. The proposed QRS detection method by using the spatial velocity was identified as having the 99.6% detection accuracy as well as fast processing time. Atrial flutter, coupled P wave, and noncoupled P wave as well as atrial fibrillation could be detected correctly by three different algorithms according to their origination farm. About 99.6% correction accuracy coupled P wave could be obtained and we could be found that most detection errors are caused by establishing wrong search interval.

• Journal of Biomedical Engineering Research
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• v.20 no.4
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• pp.435-442
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• 1999

A fuzzy-ART(adaptive resonance theory) network for the PVC(premature ventricular contraction) classification using wavelet coefficient is designed. This network consists of the feature extraction and learning of the fuzzy-ART network. In the first step, we have detected the QRS from the ECG signal in order to set the threshold range for feature extraction and the detected QRS was divided into several frequency bands by wavelet transformation using Haar wavelet. Among the low-frequency bands, only the 6th coefficient(D6) are selected as the input feature. After that, the fuzzy-ART network for classification of the PVC is learned by using input feature which comprises of binary data converted by applying threshold to D6. The MIT/BIH database including the PVC is used for the evaluation. The designed fuzzy-ART network showed the PVC classification ratio of 96.52%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.117-126
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• 2019

Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.