• 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.19 no.1
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• pp.192-200
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• 2015

Several algorithms have been developed to classify arrhythmia which 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 classify the pattern by analyzing personalized ECG signal and extracting minimal feature. Thus, QRS pattern Analysis of personalized ECG Signal by Q, R, S peak variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and extract eight feature by amplitude and phase variability. Also, we classified nine pattern in realtime through peak and morphology variability. PVC, PAC, Normal, LBBB, RBBB, Paced beat arrhythmia is evaluated by using 43 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 93.72% in QRS pattern detection classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.427-436
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• 2016

In general, QRS duration represent a distance of Q start and S end point. However, since criteria of QRS duration are vague and Q, S point is not detected accurately, arrhythmia classification performance can be reduced. In this paper, we propose extraction of Q, S start and end point RS feature based on phase transition tracking method after we detected R wave that is large peak of electrocardiogram(ECG) signal. For this purpose, we detected R wave, from noise-free ECG signal through the preprocessing method. Also, we classified QRS pattern through differentiation value of ECG signal and extracted Q, S start and end point by tracking direction and count of phase based on R wave. The performance of R wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. The achieved scores indicate the average detection rate of 99.60%. PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 premature ventricular contraction(PVC). The achieved scores indicate the average detection rate of 94.12% in PVC.