An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique
| العنوان: | An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique |
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| المؤلفون: | Wahyu Caesarendra, Muhammad Naufal Rahmatullah, Vicko Bhayyu, Radiyati Umi Partan, Siti Nurmaini, Firdaus Firdaus, Tresna Dewi, Annisa Darmawahyuni |
| المصدر: | Applied Sciences Volume 9 Issue 14 Applied Sciences, Vol 9, Iss 14, p 2921 (2019) |
| بيانات النشر: | Multidisciplinary Digital Publishing Institute, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | cardiac disease, Computer science, Feature extraction, Feature selection, 02 engineering and technology, lcsh:Technology, lcsh:Chemistry, 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, Instrumentation, lcsh:QH301-705.5, 030304 developmental biology, Fluid Flow and Transfer Processes, 0303 health sciences, Artificial neural network, Receiver operating characteristic, business.industry, lcsh:T, Process Chemistry and Technology, Deep learning, General Engineering, Confusion matrix, deep learning, Pattern recognition, lcsh:QC1-999, unsupervised feature learning, Computer Science Applications, lcsh:Biology (General), lcsh:QD1-999, classification, lcsh:TA1-2040, 020201 artificial intelligence & image processing, Artificial intelligence, business, lcsh:Engineering (General). Civil engineering (General), Classifier (UML), Feature learning, lcsh:Physics |
| الوصف: | An automated classification system based on a Deep Learning (DL) technique for Cardiac Disease (CD) monitoring and detection is proposed in this paper. The proposed DL architecture is divided into Deep Auto-Encoders (DAEs) as an unsupervised form of feature learning and Deep Neural Networks (DNNs) as a classifier. The objective of this study is to improve on the previous machine learning technique that consists of several data processing steps such as feature extraction and feature selection or feature reduction. It is also noticed that the previously used machine learning technique required human interference and expertise in determining robust features, yet was time-consuming in the labeling and data processing steps. In contrast, DL enables an embedded feature extraction and feature selection in DAEs pre-training and DNNs fine-tuning process directly from raw data. Hence, DAEs is able to extract high-level of features not only from the training data but also from unseen data. The proposed model uses 10 classes of imbalanced data from ECG signals. Since it is related to the cardiac region, abnormality is usually considered for an early diagnosis of CD. In order to validate the result, the proposed model is compared with the shallow models and DL approaches. Results found that the proposed method achieved a promising performance with 99.73% accuracy, 91.20% sensitivity, 93.60% precision, 99.80% specificity, and a 91.80% F1-Score. Moreover, both the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR) curve from the confusion matrix showed that the developed model is a good classifier. The developed model based on unsupervised feature extraction and deep neural network is ready to be used on a large population before its installation for clinical usage. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2076-3417 |
| DOI: | 10.3390/app9142921 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::da5e125e240fc48ea0e78d373af8fdd2 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....da5e125e240fc48ea0e78d373af8fdd2 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20763417 |
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| DOI: | 10.3390/app9142921 |