ECG heartbeat classification using progressive moving average transform
Abstract This paper presents the Progressive Moving Average Transform (PMAT), a novel signal transformation method for converting time-domain signals into 2D representations by progressively computing Moving Averages (MAs) with varying window sizes. The approach aims to enhance signal analysis and c...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-88119-9 |
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| _version_ | 1823862329228394496 |
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| author | Rabah Mokhtari Samir Brahim Belhouari Khelil Kassoul Abderraouf Hocini |
| author_facet | Rabah Mokhtari Samir Brahim Belhouari Khelil Kassoul Abderraouf Hocini |
| author_sort | Rabah Mokhtari |
| collection | DOAJ |
| description | Abstract This paper presents the Progressive Moving Average Transform (PMAT), a novel signal transformation method for converting time-domain signals into 2D representations by progressively computing Moving Averages (MAs) with varying window sizes. The approach aims to enhance signal analysis and classification, particularly in the context of heartbeat classification. Our approach integrates PMAT with a 2D-Convolutional Neural Network (CNN) model for the classification of ECG heartbeat signals. The 2D-CNN model is employed to extract meaningful features from the transformed 2D representations and classify them efficiently. To assess the effectiveness of our approach, we conducted extensive simulations utilizing three widely-used databases: the MIT-BIH database and the INCART database, chosen to cover a wide range of heartbeats. Our experiments involved classifying more than 6 heartbeat types grouped into three main classes. Results indicate high accuracy and F1-scores, with 99.09% accuracy and 92.13% F1-score for MIT-BIH, and 98.37% accuracy and 79.37% F1-score for INCART. Notably, the method demonstrates robustness when trained on one database and tested on another, achieving accuracy rates exceeding 95% in both cases. Specifically, the method achieves 96% accuracy when trained on MIT-BIH and tested on the ST-T European database. These findings underscore the effectiveness and stability of the proposed approach in accurately classifying heartbeats across different datasets, suggesting its potential for practical implementation in medical diagnostics and healthcare systems. |
| format | Article |
| id | doaj-art-aaac72c68cab4d699ac3d43063f16051 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-aaac72c68cab4d699ac3d43063f160512025-02-09T12:35:42ZengNature PortfolioScientific Reports2045-23222025-02-0115111610.1038/s41598-025-88119-9ECG heartbeat classification using progressive moving average transformRabah Mokhtari0Samir Brahim Belhouari1Khelil Kassoul2Abderraouf Hocini3Computer Science Department, Faculty of Mathematics and Computer Science, University of M’silaDivision of Information and Computing Technology, College of Science and Engineering, Hamad Ben Khalifa UniversityGeneva School of Business Administration, University of Applied Sciences Western Switzerland HES-SOComputer Science Department, University of M’silaAbstract This paper presents the Progressive Moving Average Transform (PMAT), a novel signal transformation method for converting time-domain signals into 2D representations by progressively computing Moving Averages (MAs) with varying window sizes. The approach aims to enhance signal analysis and classification, particularly in the context of heartbeat classification. Our approach integrates PMAT with a 2D-Convolutional Neural Network (CNN) model for the classification of ECG heartbeat signals. The 2D-CNN model is employed to extract meaningful features from the transformed 2D representations and classify them efficiently. To assess the effectiveness of our approach, we conducted extensive simulations utilizing three widely-used databases: the MIT-BIH database and the INCART database, chosen to cover a wide range of heartbeats. Our experiments involved classifying more than 6 heartbeat types grouped into three main classes. Results indicate high accuracy and F1-scores, with 99.09% accuracy and 92.13% F1-score for MIT-BIH, and 98.37% accuracy and 79.37% F1-score for INCART. Notably, the method demonstrates robustness when trained on one database and tested on another, achieving accuracy rates exceeding 95% in both cases. Specifically, the method achieves 96% accuracy when trained on MIT-BIH and tested on the ST-T European database. These findings underscore the effectiveness and stability of the proposed approach in accurately classifying heartbeats across different datasets, suggesting its potential for practical implementation in medical diagnostics and healthcare systems.https://doi.org/10.1038/s41598-025-88119-9 |
| spellingShingle | Rabah Mokhtari Samir Brahim Belhouari Khelil Kassoul Abderraouf Hocini ECG heartbeat classification using progressive moving average transform Scientific Reports |
| title | ECG heartbeat classification using progressive moving average transform |
| title_full | ECG heartbeat classification using progressive moving average transform |
| title_fullStr | ECG heartbeat classification using progressive moving average transform |
| title_full_unstemmed | ECG heartbeat classification using progressive moving average transform |
| title_short | ECG heartbeat classification using progressive moving average transform |
| title_sort | ecg heartbeat classification using progressive moving average transform |
| url | https://doi.org/10.1038/s41598-025-88119-9 |
| work_keys_str_mv | AT rabahmokhtari ecgheartbeatclassificationusingprogressivemovingaveragetransform AT samirbrahimbelhouari ecgheartbeatclassificationusingprogressivemovingaveragetransform AT khelilkassoul ecgheartbeatclassificationusingprogressivemovingaveragetransform AT abderraoufhocini ecgheartbeatclassificationusingprogressivemovingaveragetransform |