کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6920374 | 1447919 | 2018 | 21 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine
ترجمه فارسی عنوان
شناخت اتوماتیک آریتمی بر اساس شبکه تجزیه و تحلیل مولفه اصلی و دستگاه بردار پشتیبانی خطی
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کلمات کلیدی
شبکه تجزیه و تحلیل مولفه اصلی، تشخیص آریتمی، استحکام نویز، یادگیری عمیق، بیماری های قلبی عروقی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Electrocardiogram (ECG) classification is an important process in identifying arrhythmia, and neural network models have been widely used in this field. However, these models are often disrupted by heartbeat noise and are negatively affected by skewed data. To address these problems, a novel heartbeat recognition method is presented. The aim of this study is to apply a principal component analysis network (PCANet) for feature extraction based on a noisy ECG signal. To improve the classification speed, a linear support vector machine (SVM) was applied. In our experiments, we identified five types of imbalanced original and noise-free ECGs in the MIT-BIH arrhythmia database to verify the effectiveness of our algorithm and achieved 97.77% and 97.08% accuracy, respectively. The results show that our method has high recognition accuracy in the classification of skewed and noisy heartbeats, indicating that our method is a practical ECG recognition method with suitable noise robustness and skewed data applicability.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Biology and Medicine - Volume 101, 1 October 2018, Pages 22-32
Journal: Computers in Biology and Medicine - Volume 101, 1 October 2018, Pages 22-32
نویسندگان
Weiyi Yang, Yujuan Si, Di Wang, Buhao Guo,