Optimizing machine learning for enhanced automated ECG analysis in cardiovascular healthcare
The rapid advancement of connected health technology, exemplified by wearable devices like the Apple Watch, has revolutionized healthcare by enhancing the diagnosis, monitoring, and treatment of various conditions, particularly heart-related issues. However, these devices generate vast amounts of EC...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Egyptian Informatics Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866524001415 |
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| _version_ | 1846122147821387776 |
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| author | Keyi Tang Shuyuan Ma Xiaohui Sun Dongfang Guo |
| author_facet | Keyi Tang Shuyuan Ma Xiaohui Sun Dongfang Guo |
| author_sort | Keyi Tang |
| collection | DOAJ |
| description | The rapid advancement of connected health technology, exemplified by wearable devices like the Apple Watch, has revolutionized healthcare by enhancing the diagnosis, monitoring, and treatment of various conditions, particularly heart-related issues. However, these devices generate vast amounts of ECG data that require interpretation, underscoring the need for reliable automated ECG analysis methods. This study explores the use of machine learning and deep learning algorithms, including Support Vector Classifier (SVC), RandomForest, XGBoost, and LinearSVC, for ECG classification, aiming to improve accuracy and diagnostic capabilities. While traditional methods rely on heuristic features and shallow architectures, this research focuses on leveraging deep learning architectures to automatically extract relevant features from ECG signals. The proposed approach demonstrates promising results in accurately categorizing heartbeats, offering a potential solution to the limitations of current classification methods. By optimizing classification models with metaheuristic algorithms, such as JADE, the study achieves significant performance improvements, highlighting the effectiveness of integrating advanced optimization techniques into ECG analysis processes. Ultimately, the findings underscore the potential of machine learning and deep learning algorithms in advancing automated ECG analysis for improved cardiovascular healthcare. |
| format | Article |
| id | doaj-art-f16d9f233c6b435f998ea3e624f31ffd |
| institution | Kabale University |
| issn | 1110-8665 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-f16d9f233c6b435f998ea3e624f31ffd2024-12-15T06:14:51ZengElsevierEgyptian Informatics Journal1110-86652024-12-0128100578Optimizing machine learning for enhanced automated ECG analysis in cardiovascular healthcareKeyi Tang0Shuyuan Ma1Xiaohui Sun2Dongfang Guo3Qingdao Fifth People's Hospital, Shandong 266002, ChinaQingdao Fifth People's Hospital, Shandong 266002, ChinaQingdao Central Hospital obstetrical department, Shandong 266002, ChinaQingdao Fifth People's Hospital, Shandong 266002, China; Corresponding author.The rapid advancement of connected health technology, exemplified by wearable devices like the Apple Watch, has revolutionized healthcare by enhancing the diagnosis, monitoring, and treatment of various conditions, particularly heart-related issues. However, these devices generate vast amounts of ECG data that require interpretation, underscoring the need for reliable automated ECG analysis methods. This study explores the use of machine learning and deep learning algorithms, including Support Vector Classifier (SVC), RandomForest, XGBoost, and LinearSVC, for ECG classification, aiming to improve accuracy and diagnostic capabilities. While traditional methods rely on heuristic features and shallow architectures, this research focuses on leveraging deep learning architectures to automatically extract relevant features from ECG signals. The proposed approach demonstrates promising results in accurately categorizing heartbeats, offering a potential solution to the limitations of current classification methods. By optimizing classification models with metaheuristic algorithms, such as JADE, the study achieves significant performance improvements, highlighting the effectiveness of integrating advanced optimization techniques into ECG analysis processes. Ultimately, the findings underscore the potential of machine learning and deep learning algorithms in advancing automated ECG analysis for improved cardiovascular healthcare.http://www.sciencedirect.com/science/article/pii/S1110866524001415ECG datasetXGBoostingJADEClassificationMachine learning |
| spellingShingle | Keyi Tang Shuyuan Ma Xiaohui Sun Dongfang Guo Optimizing machine learning for enhanced automated ECG analysis in cardiovascular healthcare Egyptian Informatics Journal ECG dataset XGBoosting JADE Classification Machine learning |
| title | Optimizing machine learning for enhanced automated ECG analysis in cardiovascular healthcare |
| title_full | Optimizing machine learning for enhanced automated ECG analysis in cardiovascular healthcare |
| title_fullStr | Optimizing machine learning for enhanced automated ECG analysis in cardiovascular healthcare |
| title_full_unstemmed | Optimizing machine learning for enhanced automated ECG analysis in cardiovascular healthcare |
| title_short | Optimizing machine learning for enhanced automated ECG analysis in cardiovascular healthcare |
| title_sort | optimizing machine learning for enhanced automated ecg analysis in cardiovascular healthcare |
| topic | ECG dataset XGBoosting JADE Classification Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S1110866524001415 |
| work_keys_str_mv | AT keyitang optimizingmachinelearningforenhancedautomatedecganalysisincardiovascularhealthcare AT shuyuanma optimizingmachinelearningforenhancedautomatedecganalysisincardiovascularhealthcare AT xiaohuisun optimizingmachinelearningforenhancedautomatedecganalysisincardiovascularhealthcare AT dongfangguo optimizingmachinelearningforenhancedautomatedecganalysisincardiovascularhealthcare |