Comparison of shallow and deep learning methods of ECG signals clas-sification for arrhythmia detection
The research aimed to compare the classification performance of arrhythmia classification from the ECG signal dataset from the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) database. Shallow learning methods that were used in this study are Support Vector Machine, Naïve Baye...
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| Format: | Article |
| Language: | English |
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Lublin University of Technology
2023-06-01
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| Series: | Journal of Computer Sciences Institute |
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| Online Access: | https://ph.pollub.pl/index.php/jcsi/article/view/3273 |
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| _version_ | 1849387882152722432 |
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| author | Dodon Turianto Nugrahadi Rudy Herteno Dwi Kartini Muhammad Haekal Mohammad Reza Faisal |
| author_facet | Dodon Turianto Nugrahadi Rudy Herteno Dwi Kartini Muhammad Haekal Mohammad Reza Faisal |
| author_sort | Dodon Turianto Nugrahadi |
| collection | DOAJ |
| description |
The research aimed to compare the classification performance of arrhythmia classification from the ECG signal dataset from the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) database. Shallow learning methods that were used in this study are Support Vector Machine, Naïve Bayes, and Random Forest. 1D Convolutional Neural Network (1D CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) were deep learning methods that were used for the study. The models were tested on a dataset with 140 samples that were grouped into four class labels, and each sample has 2160 features. Those models were tested for classification performance. This research shows Random Forest and 1D CNN have the best performance.
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| format | Article |
| id | doaj-art-f313b1d5cb7a4d0c8256b6e6ea4c1909 |
| institution | Kabale University |
| issn | 2544-0764 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Lublin University of Technology |
| record_format | Article |
| series | Journal of Computer Sciences Institute |
| spelling | doaj-art-f313b1d5cb7a4d0c8256b6e6ea4c19092025-08-20T03:42:28ZengLublin University of TechnologyJournal of Computer Sciences Institute2544-07642023-06-012710.35784/jcsi.3273Comparison of shallow and deep learning methods of ECG signals clas-sification for arrhythmia detectionDodon Turianto Nugrahadi0https://orcid.org/0000-0001-7746-2658Rudy HertenoDwi Kartinihttps://orcid.org/0000-0002-7382-5084Muhammad HaekalMohammad Reza Faisalhttps://orcid.org/0000-0001-5748-7639Lambung Mangkurat University The research aimed to compare the classification performance of arrhythmia classification from the ECG signal dataset from the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) database. Shallow learning methods that were used in this study are Support Vector Machine, Naïve Bayes, and Random Forest. 1D Convolutional Neural Network (1D CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) were deep learning methods that were used for the study. The models were tested on a dataset with 140 samples that were grouped into four class labels, and each sample has 2160 features. Those models were tested for classification performance. This research shows Random Forest and 1D CNN have the best performance. https://ph.pollub.pl/index.php/jcsi/article/view/3273ECG signalsarrhythmia classificationshallow learningdeep learning |
| spellingShingle | Dodon Turianto Nugrahadi Rudy Herteno Dwi Kartini Muhammad Haekal Mohammad Reza Faisal Comparison of shallow and deep learning methods of ECG signals clas-sification for arrhythmia detection Journal of Computer Sciences Institute ECG signals arrhythmia classification shallow learning deep learning |
| title | Comparison of shallow and deep learning methods of ECG signals clas-sification for arrhythmia detection |
| title_full | Comparison of shallow and deep learning methods of ECG signals clas-sification for arrhythmia detection |
| title_fullStr | Comparison of shallow and deep learning methods of ECG signals clas-sification for arrhythmia detection |
| title_full_unstemmed | Comparison of shallow and deep learning methods of ECG signals clas-sification for arrhythmia detection |
| title_short | Comparison of shallow and deep learning methods of ECG signals clas-sification for arrhythmia detection |
| title_sort | comparison of shallow and deep learning methods of ecg signals clas sification for arrhythmia detection |
| topic | ECG signals arrhythmia classification shallow learning deep learning |
| url | https://ph.pollub.pl/index.php/jcsi/article/view/3273 |
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