• 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).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.654-661
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• 2011

This paper is about a personal identification algorithm using an ECG that has been studied by a few researchers recently. Previously published algorithm can be classified as two methods. One is the method that analyzes of ECG features and the other is the morphological analysis of ECG. The main characteristic of proposed algorithm can be classified the method of analysis ECG features. Proposed algorithm adopts DSTW(Down Slope Trace Wave) for extracting ECG features, and applies SVM(Support Vector Machine) to training and testing as a classifier algorithm. We choose 18 ECG files from MIT-BIH Normal Sinus Rhythm Database for estimating of algorithm performance. The algorithm extracts 100 heartbeats from each ECG file, and use 40 heartbeats for training and 60 heartbeats for testing. The proposed algorithm shows clearly superior performance in all ECG data, amounting to 93.89% heartbeat recognition rate and 100% ECG recognition rate.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.4
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• pp.290-295
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• 2016

This paper proposes a new method for the automatic detection of atrial fibrillation (AF), using Markov regime switching GARCH (1, 1) model. The proposed method is based on the observation that variability patterns of heart rate intervals during AF significantly differ from regular patterns. The proposed method captures the different patterns of heart rate intervals between two regimes : normal and AF states. We test the proposed method using Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) atrial fibrillation database, and demonstrate the effectiveness of the proposed method.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.221-224
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• 1996

This paper presents the ECG data compression using wavelet transform(WT) and adaptive fractal interpolation(AFI). The WT has the subband coding scheme. The fractal compression method represents any range of ECG signal by fractal interpolation parameters. Specially, the AFI used the adaptive range sizes and got good performance for ECG data compression. In this algorithm, the AFI is applied into the low frequency part of WT. The MIT/BIH arrhythmia data was used for evaluation. The compression rate using WT and AFI algorithm is better than the compression rate using AFI. The WT and AFI algorithm yields compression ratio as high as 21.0 without any entroy coding.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1924-1925
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• 2007

본 논문에서는 부정맥 판별을 위한 전처리 과정으로 PCA, LDA, ICA를 바탕으로 하여 정확도를 비교하여 보았다. 각각의 전처리는 고유의 특성을 가지고 있으며 본 논문의 목적은 부정맥 판별상 어떤 전처리가 더욱 정확성의 면에서 효과적인지를 알아보는 것이다. 본 논문의 데이터는 MIT-BIH에 기반하고 있으며, Beat의 분류는 정상(Normal), 좌각차단(Left Bundle Branch Block, LBBB), 우각차단(Right Bundle Branch Block, RBBB), 조기심실수축(Premature Ventricular Contraction, PVC), 조기심방수축(Atrial Premature Beat, APB), paced Beat, 심실보충수축(Ventricular Escape Beat)로 나누었다. 실험적 결과는 PCA-BPNN의 경우 95.53%, ICA-BPNN의 경우 93.95%, LDA-BPNN의 경우 96.42%로 LDA가 가장 ECG 부정맥 판별 응용에 있어 가장 효율적인 방법으로 나타났다.

• Journal of Biomedical Engineering Research
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• v.21 no.5
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• pp.495-503
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• 2000

스트레스 심전계에서 발생되는 근잡음을 제거하기 위하여 wavelet interpolation filter(WIF)를 설계하였다. WIF는 크게 웨이브렛 변환부와 보간법 적용부로 구성되어 있다. 웨이브렛 변환부는 Haar 웨이브렛을 이용하였으며 심전도 저주파 영역과 고주파 영역으로 분할하는 과정이다. 보간법 적용부에서는 분할되어진 신호 중 A3을 선택하여 신호의 재생 성능을 향상시키기 위하여 보간법을 적용하였다. WIF의 성능을 평가하기 위해서 신호대 잡음비, 재생신호 자승오차 및 표준편차의 파라미터를 이용하였다. 본 실험에서는 MIT/BIH 부정맥 데이터베이스, European ST-T 데이터베이스 및 삼각파형을 이용하여 성능 파라미터를 측정하였다. 결과적으로 WIF는 성능 파라미터에서 기존에 많이 사용되고 있는 평균값 필터, 중간값 필터 및 hard thresholding 방법에 비해 우수함을 알 수 있었다.

• Journal of Sensor Science and Technology
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• v.28 no.1
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• pp.13-16
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• 2019

The analysis of electrocardiogram (ECG) signals facilitates the detection of various abnormal conditions of the human heart. The QRS complex is the most critical part of the ECG waveform. Further, different diseases can be identified based on the QRS complex. In this paper, a new algorithm based on the well-known Pan-Tompkins algorithm has been proposed. In the proposed scheme, the QRS complex is initially extracted by removing the background noise. Subsequently, the R-R interval and heart rate are calculated to detect whether the ECG is normal or has some abnormalities such as tachycardia and bradycardia. The accuracy of the proposed algorithm is found to be almost the same as the Pan-Tompkins algorithm and increases the R peak detection processing speed. For this work, samples are used from the MIT-BIH Arrhythmia Database, and the simulation is carried out using MATLAB 2016a.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.2105-2112
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• 2010

The noise component of electrocardiogram is not distributed in a certain frequency band. It is expressed in various types of signals by rater's physical and environmental conditions. Particularly, since the baseline wander is occurred by the mixture of the original signal and 0 ~ 2 [Hz] range of the frequency components according to muscle constraction of part attaching to an electrode and respiration rythm, it makes the ECG signal analysis difficult. Several methods have been proposed to eliminate the wandering effectually. However, they have some problems. In some methods, the high processing time is required due to the computational complexity, while in other cases ECG signal morphology can be distorted. This paper suggests a simple and effective algorithm that eliminates baseline wander with low computational complexity and without distorting signal morphology. First, the algorithm detects and segments a baseline wandering interval using slope analysis and curve point detection, second, approximates the wandering in the interval as a sinusoid, and then subtracts the sinusoid from signal. Finally, ecg signals without baseline wander are obtained. In order to evaluate the performance of the algorithm, several ECG signals with baseline wandering in MIT/BIH ECG database 101, 111, 113, 234 record were chosen and applied to the algorithm. It is found that the algorithm removes baseline wanders effectively without significant morphological distortion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1587-1595
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• 2016

P wave is cardiac parameters that represent the electrical and physiological characteristics, it is very important to diagnose atrial arrhythmia. However, It is very difficult to detect because of the small size compared to R wave and the various morphology. Several methods for detecting P wave has been proposed, such as frequency analysis and non-linear approach. However, in the case of conduction abnormality such as AV block or atrial arrhythmia, detection accuracy is at the lower level. We propose P wave detection algorithm through adaptive threshold and QRS peak variability. For this purpose, we detected Q, R, S wave from noise-free ECG signal through the preprocessing method. And then we classified three pattern of P wave by peak variability and detected adaptive window and threshold. The performance of P wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. The achieved scores indicate the average detection rate of 92.60%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.825-832
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• 2014

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. Even if some methods have the advantage in low complexity, but they generally suffer form low sensitivity. Also, it is difficult to detect PVC accurately because of the various QRS pattern by person's individual difference. Therefore it is necessary to design an efficient algorithm that classifies PVC based on QRS pattern in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose PVC classification based on QRS pattern using QS interval and R wave amplitude. For this purpose, we detected R wave, RR interval, QRS pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 PVC. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 93.72% in PVC classification.