این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Journal of Medical Signals and Sensors، جلد ۱۱، شماره ۳، صفحات ۱۸۵-۱۹۳
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Detection and classification of myocardial infarction with support vector machine classifier using grasshopper optimization algorithm
چکیده انگلیسی مقاله Background: Providing a noninvasive, rapid, and cost-effective approach to diagnose of myocardial infarction (MI) is essential in the early stages of electrocardiogram (ECG) signaling. In this article, we proposed the new optimization method for support vector machine (SVM) classifier to MI classification. Methods: After preprocessing ECG signal and noise removal, three features such as Q-wave integral, T-wave integral, and QRS-complex integral have been extracted in this study. After that, different statistical tests have evaluated the matrix of these features. To more accurately detect and classify the MI disease, optimizing the SVM classification parameters using the grasshopper optimization algorithm (GOA) was first used in this study (that called SVM-GOA). Results: After applying the GOA on the SVM classifier for all three kernels, the final results of MI detection for sensitivity, specificity, and accuracy were 100% ± 0%, 100% ± 0%, and 100% ± 0%, respectively. The final results of different MI types' classification after applying the GOA on SVM for polynomial kernel were obtained 100% ± 0%, 97.37% ± 0%, and 94.2% ± 0.2% for sensitivity and specificity and accuracy, respectively. However, the results of both linear and RBF kernels that were used for the SVM classifier method have also shown a significant increase after using GOA. Conclusion: This article's results show the highly desirable effect of applying a GOA to optimize different kernel parameters used in the SVM classifier for accurate detection and classification of MI. The proposed algorithm's final results show that the proposed system has a relatively higher performance than other previous studies.
کلیدواژه‌های انگلیسی مقاله Biomedical signal processing, electrocardiogram, grasshopper optimization algorithm, myocardial infarction, support vector machine classifier
نویسندگان مقاله | Naser Safdarian
School of Medicine, Dezful University of Medical Sciences, Dezful, Iran


| Shadi Yoosefian Nezhad
Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran


| Nader Jafarnia Dabanloo


نشانی اینترنتی http://jmss.mui.ac.ir/index.php/jmss/article/view/581
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده Original Articles
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات