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Mazidi, Mohamad Hadi, Eshghi, Mohammad, Raoufy, Mohammad Reza. (1400). Premature Ventricular Contraction (PVC) Detection System Based on Tunable Q-Factor Wavelet Transform. سامانه مدیریت نشریات علمی, 12(1), 61-74. doi: 10.31661/jbpe.v0i0.1235
Mohamad Hadi Mazidi; Mohammad Eshghi; Mohammad Reza Raoufy. "Premature Ventricular Contraction (PVC) Detection System Based on Tunable Q-Factor Wavelet Transform". سامانه مدیریت نشریات علمی, 12, 1, 1400, 61-74. doi: 10.31661/jbpe.v0i0.1235
Mazidi, Mohamad Hadi, Eshghi, Mohammad, Raoufy, Mohammad Reza. (1400). 'Premature Ventricular Contraction (PVC) Detection System Based on Tunable Q-Factor Wavelet Transform', سامانه مدیریت نشریات علمی, 12(1), pp. 61-74. doi: 10.31661/jbpe.v0i0.1235
Mazidi, Mohamad Hadi, Eshghi, Mohammad, Raoufy, Mohammad Reza. Premature Ventricular Contraction (PVC) Detection System Based on Tunable Q-Factor Wavelet Transform. سامانه مدیریت نشریات علمی, 1400; 12(1): 61-74. doi: 10.31661/jbpe.v0i0.1235

Premature Ventricular Contraction (PVC) Detection System Based on Tunable Q-Factor Wavelet Transform

Journal of Biomedical Physics and Engineering
مقاله 7، دوره 12، شماره 1، اردیبهشت 2022، صفحه 61-74    اصل مقاله (1.18 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.1235
نویسندگان
Mohamad Hadi Mazidi* 1؛ Mohammad Eshghi2؛ Mohammad Reza Raoufy3
1PhD, Department of Electric Engineering, Qeshm branch, Islamic Azad University, Qeshm, Iran
2PhD, Department of Electronics, Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
3PhD, Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
چکیده
Background: The Electrocardiogram (ECG) is an important measure for diagnosing the presence or absence of heart arrhythmias. Premature ventricular contractions (PVC) is a relatively large arrhythmia occurring outside the normal tract and being triggered outside the Sino atrial (SA) node of heart.
Objective: This study has focused on tunable Q-factor wavelet transform (TQWT) algorithm and statistical methods to detect PVC.
Material and Methods: In this analytical and statistical study, 22 ECGs records were selected from the MIT/BIH arrhythmia database. In the first stage the noise of signal remove and then five sub-bands create by TQWT. In the second stage nine features (minimum, maximum, root mean square, mean, interquartile range, standard deviation (SD), skewness, and variance) extracted of ECG and then the best features selected by using analysis of variance (ANOVA) test. Finally, the system is evaluated by using the learning machines of support vector machine (SVM), the K-Nearest Neighbor (KNN), and artificial neural network (ANN).
Results: The best results were verified with KNN learning machine: the sensitivity Se= 98.23% and accuracy Ac= 97.81%.
Conclusion: A comparative analysis with the related existing methods shows the method proposed in this study is higher than the other method for classification PVC and can help physicians to classify normal and PVC heart signals in the screening of the patients with coronary artery diseases (CADs).
کلیدواژه‌ها
Tunable Q-factor Wavelet Transform (TQWT)؛ Electrocardiogram (ECG)؛ Algorithms؛ Wavelet Analysis؛ Support Vector Machine
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