• 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.

• 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 Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.447-454
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• 2011

Power-line interference(PLI) can distort certain regions in analysing the ECG signal. In particular, the regions such as P and R wave that are important element in diagnosing with arrhythmia is expressed as different type of noise according to the case whether power-line frequency is multiples of sampling frequency and or not. Noise characteristics is also divided into linearity and non-linearity. In this paper, adaptive subtraction method for removing variable PLI of ECG signal is proposed. We classify the multiple relationship between power line and sampling frequency as Multiple and Non-multiple. PLI of Linear segment is extracted through moving average filter, PLI of non-linear segment is extracted through the interference component that is extracted in the linear segment and stored in the temporary buffer. The performance of P wave and R wave detection is evaluated by using 119 data record of MIT-BIH arrhythmia database. The achieved scores indicate P wave detection rate of 97.91%, R wave detection rate of 96.66% and P wave detection rate of 99.01%, R wave detection rate of 97.93% accuracy respectively for Notch filter and proposed subtraction method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2551-2561
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• 2014

Several algorithms have been developed to detect AFIB(Atrial Fibrillation) which either rely on the linear and frequency analysis. But they are more complex than time time domain algorithm and difficult to get the consistent rule of irregular RR interval rhythm. In this study, we propose algorithm for optimal value detection of irregular RR interval for AFIB classification based on linear analysis. For this purpose, we detected R wave, RR interval, from noise-free ECG signal through the preprocessing process and subtractive operation method. Also, we set scope for segment length and detected optimal value and then classified AFIB in realtime through liniar analysis such as absolute deviation and absolute difference. The performance of proposed algorithm for AFIB classification is evaluated by using MIT-BIH arrhythmia and AFIB database. The optimal value indicate ${\alpha}=0.75$, ${\beta}=1.4$, ${\gamma}=300ms$ in AFIB classification.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.96-101
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• 2011

In recent days, the demand for the remote ECG monitoring system has been increasing and the automation of the monitoring system is becoming quite of a concern. Automatic detection of the abnormal ECG beats must be a necessity for the successful commercialization of these real time remote ECG monitoring system. From these viewpoints, in this paper, we proposed an automatic detection algorithm for the abnormal ECG beats using QRS width and RR interval patterns. In the previous research, many efforts have been done to classify the ECG beats into detailed categories. But, these approaches have disadvantages such that they produce lots of misclassification errors and variabilities in the classification performance. Also, they require large amount of training data for the accurate classification and heavy computation during the classification process. But, we think that the detection of abnormality from the ECG beats is more important that the detailed classification for the automatic ECG monitoring system. In this paper, we tried to detect the VEB which is most frequently occurring among the abnormal ECG beats and we could achieve satisfactory detection performance when applied the proposed algorithm to the MIT/BIH database.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.62-69
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• 2022

An electrocardiogram (ECG) is one of the most important biosignals, and in particular, continuous ECG monitoring is very important in patients with arrhythmia. There are many different types of arrhythmia (sinus node, sinus tachycardia, atrial premature beat (APB), and ventricular fibrillation) depending on the cause, and continuous ECG monitoring during daily life is very important for early diagnosis of arrhythmias and setting treatment directions. The ECG signal of arrhythmia patients is very unstable, and it is difficult to detect the R-peak point, which is a key feature for automatic arrhythmias detection. In this study, we develped a continuous measuring Holter ECG monitoring device and software for analysis and confirmed the utility of R-peak of the ECG signal with MIT-BIH arrhythmia database. In future studies, it needs the validation of algorithms and clinical data for morphological classification and prediction of arrhythmias due to various etiologies.