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، جلد ۱۱، شماره ۲، صفحات ۲۵۵-۲۶۴
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
A Deep Learning Approach for Detecting Atrial Fibrillation using RR Intervals of ECG |
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| چکیده انگلیسی مقاله |
Atrial Fibrillation (AF) is one of the most common type of heart arrhythmias observed in the clinical practice. AF can be detected using Electrocardiogram (ECG). ECG signal are time varying and nonlinear in nature. Hence, it is very difficult for a physician to perform accurate and rapid classification of different heart rhythms, manually. In this paper we propose a method using Discrete Wavelet Transform (DWT) with db6 as basis function for denoising ECG signal along with Savitzky- Golay filter to smoothen the signal. Deep learning methods, such as combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) (CNN-LSTM) and ResNet18 are used for accurate classification of ECG signal using Physionet Challenge 2017 database. With 10-fold cross validation method the model provided overall accuracy of 98.25% with CNN-LSTM classifier. |
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| کلیدواژه‌های انگلیسی مقاله |
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| نویسندگان مقاله |
| Shrikanth Rao S.K
Department of Electronics & Communication Engineering, Vivekananda College of Engineering & Technology, Puttur, Dakshina Kannada, India
| MaheshKumar H Kolekar
Department of Electrical Engineering, Indian Institute of Technology, Patna, India
| Roshan Joy Martis
Department of Computer Science and Engineering, Global Academy of Technology, Bangalore, India
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| نشانی اینترنتی |
https://fbt.tums.ac.ir/index.php/fbt/article/view/555 |
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