Deep learning with Bayesian Hyperparameter Optimization for Precise Electrocardiogram Signals Delineation
Electrocardiography (ECG) serves as an essential risk-stratification tool to observe further treatment for cardiac abnormalities. The cardiac abnormalities are indicated by the intervals and amplitude locations in the ECG waveform. ECG delineation plays a crucial role in identifying the critical poi...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2025-04-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6171 |
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| author | Annisa Darmawahyuni Winda Kurnia Sari Nurul Afifah Siti Nurmaini Jordan Marcelino Rendy Isdwanta |
| author_facet | Annisa Darmawahyuni Winda Kurnia Sari Nurul Afifah Siti Nurmaini Jordan Marcelino Rendy Isdwanta |
| author_sort | Annisa Darmawahyuni |
| collection | DOAJ |
| description | Electrocardiography (ECG) serves as an essential risk-stratification tool to observe further treatment for cardiac abnormalities. The cardiac abnormalities are indicated by the intervals and amplitude locations in the ECG waveform. ECG delineation plays a crucial role in identifying the critical points necessary for observing cardiac abnormalities based on the characteristics and features of the waveform. In this study, we propose a deep learning approach combined with Bayesian Hyperparameter Optimization (BHO) for hyperparameter tuning to delineate the ECG signal. BHO is an optimization method utilized to determine the optimal values of an objective function. BHO allows for efficient and faster parameter search compared to conventional tuning methods, such as grid search. This method focuses on the most promising search areas in the parameter space, iteratively builds a probability model of the objective function, and then uses that model to select new points to test. The used hyperparameters of BHO contain learning rate, batch size, epoch, and total of long short-term memory layers. The study resulted in the development of 40 models, with the best model achieving a 99.285 accuracy, 94.5% sensitivity, 99.6% specificity, and 94.05% precision. The ECG delineation-based deep learning with BHO shows its excellence for localization and position of the onset, peak, and offset of ECG waveforms. The proposed model can be applied in medical applications for ECG delineation. |
| format | Article |
| id | doaj-art-3af89f9a02eb4f9fbce07ec3fe77822c |
| institution | OA Journals |
| issn | 2580-0760 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-3af89f9a02eb4f9fbce07ec3fe77822c2025-08-20T02:30:13ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602025-04-019229730210.29207/resti.v9i2.61716171Deep learning with Bayesian Hyperparameter Optimization for Precise Electrocardiogram Signals DelineationAnnisa Darmawahyuni0Winda Kurnia Sari1Nurul Afifah2Siti Nurmaini3Jordan Marcelino4Rendy Isdwanta5Universitas SriwijayaUniversitas SriwijayaUniversitas SriwijayaUniversitas SriwijayaUniversitas SriwijayaUniversitas SriwijayaElectrocardiography (ECG) serves as an essential risk-stratification tool to observe further treatment for cardiac abnormalities. The cardiac abnormalities are indicated by the intervals and amplitude locations in the ECG waveform. ECG delineation plays a crucial role in identifying the critical points necessary for observing cardiac abnormalities based on the characteristics and features of the waveform. In this study, we propose a deep learning approach combined with Bayesian Hyperparameter Optimization (BHO) for hyperparameter tuning to delineate the ECG signal. BHO is an optimization method utilized to determine the optimal values of an objective function. BHO allows for efficient and faster parameter search compared to conventional tuning methods, such as grid search. This method focuses on the most promising search areas in the parameter space, iteratively builds a probability model of the objective function, and then uses that model to select new points to test. The used hyperparameters of BHO contain learning rate, batch size, epoch, and total of long short-term memory layers. The study resulted in the development of 40 models, with the best model achieving a 99.285 accuracy, 94.5% sensitivity, 99.6% specificity, and 94.05% precision. The ECG delineation-based deep learning with BHO shows its excellence for localization and position of the onset, peak, and offset of ECG waveforms. The proposed model can be applied in medical applications for ECG delineation.https://jurnal.iaii.or.id/index.php/RESTI/article/view/6171bayesian optimizationecg delineationdeep learning |
| spellingShingle | Annisa Darmawahyuni Winda Kurnia Sari Nurul Afifah Siti Nurmaini Jordan Marcelino Rendy Isdwanta Deep learning with Bayesian Hyperparameter Optimization for Precise Electrocardiogram Signals Delineation Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) bayesian optimization ecg delineation deep learning |
| title | Deep learning with Bayesian Hyperparameter Optimization for Precise Electrocardiogram Signals Delineation |
| title_full | Deep learning with Bayesian Hyperparameter Optimization for Precise Electrocardiogram Signals Delineation |
| title_fullStr | Deep learning with Bayesian Hyperparameter Optimization for Precise Electrocardiogram Signals Delineation |
| title_full_unstemmed | Deep learning with Bayesian Hyperparameter Optimization for Precise Electrocardiogram Signals Delineation |
| title_short | Deep learning with Bayesian Hyperparameter Optimization for Precise Electrocardiogram Signals Delineation |
| title_sort | deep learning with bayesian hyperparameter optimization for precise electrocardiogram signals delineation |
| topic | bayesian optimization ecg delineation deep learning |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6171 |
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