Morphological Arrhythmia Classification Based on Inter-Patient and Two Leads ECG Using Machine Learning
Arrhythmia is a heart disorder in which the heart beats irregularly. Electrocardiogram (ECG) has been widely used as a tool for detecting arrhythmias. However, the interpretation of ECG recordings is still tedious, time-consuming, and a difficult task since it needs beat-by-beat manual examination....
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IEEE
2024-01-01
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| author | Hasballah Zakaria Elsa Sari Hayunah Nurdiniyah Astri Maria Kurniawati Dziban Naufal Nana Sutisna |
| author_facet | Hasballah Zakaria Elsa Sari Hayunah Nurdiniyah Astri Maria Kurniawati Dziban Naufal Nana Sutisna |
| author_sort | Hasballah Zakaria |
| collection | DOAJ |
| description | Arrhythmia is a heart disorder in which the heart beats irregularly. Electrocardiogram (ECG) has been widely used as a tool for detecting arrhythmias. However, the interpretation of ECG recordings is still tedious, time-consuming, and a difficult task since it needs beat-by-beat manual examination. In arrhythmia classification studies, the inter-patient method is required to obtain unbiased results and has been used in most studies to detect arrhythmias specifically in large classes, such as normal, ventricular, atrial, and fusion beats. However, this method is still limited in detecting more specific arrhythmias, particularly morphological arrhythmias, such as bundle branch block and premature beats. In addition, the methods usually only use one ECG lead. To overcome this limitation, in this work, we propose inter-patient-based arrhythmias classification using combined two ECG leads automatically by employing machine learning methods, specifically ensemble learning. The classification is intended to detect 5 classes of morphological arrhythmia which are Left Bundle Branch Block (LBBB), Left Right Branch Block (RBBB), Premature Ventricular Contractions (PVC), Premature Atrial Contractions (PAC), and Normal. This work also details the method covering data set preparation, algorithm and design parameters exploration for pre-processing ECG signals, features extraction and selection, and hyper parameter tuning for employed machine learning methods. Evaluation results show that the proposed machine learning method, which is Ensemble Learning achieves performance improvements, compared to similar other works with average results of accuracy 87%, recall 87.4%, precision 88.4%, and F1-score 87%. These results correspond to performance improvements compare to state of the art methods for about 12% of accuracy, 28% of recall and 24% of precision, respectively. Furthermore, the proposed work potentially can be employed in real-world clinical practice, specifically as fast yet accurate aided tool for cardiac events diagnostic. |
| format | Article |
| id | doaj-art-18da29e05b764432bee4427cecdebe9e |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-18da29e05b764432bee4427cecdebe9e2025-08-20T01:48:02ZengIEEEIEEE Access2169-35362024-01-011214737214738610.1109/ACCESS.2024.346964010697167Morphological Arrhythmia Classification Based on Inter-Patient and Two Leads ECG Using Machine LearningHasballah Zakaria0Elsa Sari Hayunah Nurdiniyah1Astri Maria Kurniawati2https://orcid.org/0009-0003-6664-1687Dziban Naufal3Nana Sutisna4https://orcid.org/0000-0002-8435-9242School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, IndonesiaDepartment of Electrical Engineering, Universitas Jenderal Soedirman, Purwokerto, IndonesiaSchool of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, IndonesiaCenter for Health and Sport Technology, Institut Teknologi Bandung, Bandung, IndonesiaSchool of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, IndonesiaArrhythmia is a heart disorder in which the heart beats irregularly. Electrocardiogram (ECG) has been widely used as a tool for detecting arrhythmias. However, the interpretation of ECG recordings is still tedious, time-consuming, and a difficult task since it needs beat-by-beat manual examination. In arrhythmia classification studies, the inter-patient method is required to obtain unbiased results and has been used in most studies to detect arrhythmias specifically in large classes, such as normal, ventricular, atrial, and fusion beats. However, this method is still limited in detecting more specific arrhythmias, particularly morphological arrhythmias, such as bundle branch block and premature beats. In addition, the methods usually only use one ECG lead. To overcome this limitation, in this work, we propose inter-patient-based arrhythmias classification using combined two ECG leads automatically by employing machine learning methods, specifically ensemble learning. The classification is intended to detect 5 classes of morphological arrhythmia which are Left Bundle Branch Block (LBBB), Left Right Branch Block (RBBB), Premature Ventricular Contractions (PVC), Premature Atrial Contractions (PAC), and Normal. This work also details the method covering data set preparation, algorithm and design parameters exploration for pre-processing ECG signals, features extraction and selection, and hyper parameter tuning for employed machine learning methods. Evaluation results show that the proposed machine learning method, which is Ensemble Learning achieves performance improvements, compared to similar other works with average results of accuracy 87%, recall 87.4%, precision 88.4%, and F1-score 87%. These results correspond to performance improvements compare to state of the art methods for about 12% of accuracy, 28% of recall and 24% of precision, respectively. Furthermore, the proposed work potentially can be employed in real-world clinical practice, specifically as fast yet accurate aided tool for cardiac events diagnostic.https://ieeexplore.ieee.org/document/10697167/ECGmorphological arrhythmiaarrhythmia classificationinter-patientmachine learning |
| spellingShingle | Hasballah Zakaria Elsa Sari Hayunah Nurdiniyah Astri Maria Kurniawati Dziban Naufal Nana Sutisna Morphological Arrhythmia Classification Based on Inter-Patient and Two Leads ECG Using Machine Learning IEEE Access ECG morphological arrhythmia arrhythmia classification inter-patient machine learning |
| title | Morphological Arrhythmia Classification Based on Inter-Patient and Two Leads ECG Using Machine Learning |
| title_full | Morphological Arrhythmia Classification Based on Inter-Patient and Two Leads ECG Using Machine Learning |
| title_fullStr | Morphological Arrhythmia Classification Based on Inter-Patient and Two Leads ECG Using Machine Learning |
| title_full_unstemmed | Morphological Arrhythmia Classification Based on Inter-Patient and Two Leads ECG Using Machine Learning |
| title_short | Morphological Arrhythmia Classification Based on Inter-Patient and Two Leads ECG Using Machine Learning |
| title_sort | morphological arrhythmia classification based on inter patient and two leads ecg using machine learning |
| topic | ECG morphological arrhythmia arrhythmia classification inter-patient machine learning |
| url | https://ieeexplore.ieee.org/document/10697167/ |
| work_keys_str_mv | AT hasballahzakaria morphologicalarrhythmiaclassificationbasedoninterpatientandtwoleadsecgusingmachinelearning AT elsasarihayunahnurdiniyah morphologicalarrhythmiaclassificationbasedoninterpatientandtwoleadsecgusingmachinelearning AT astrimariakurniawati morphologicalarrhythmiaclassificationbasedoninterpatientandtwoleadsecgusingmachinelearning AT dzibannaufal morphologicalarrhythmiaclassificationbasedoninterpatientandtwoleadsecgusingmachinelearning AT nanasutisna morphologicalarrhythmiaclassificationbasedoninterpatientandtwoleadsecgusingmachinelearning |