Cardiac-Diagnosis: First stable release
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| Sprache: | Englisch |
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2025
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| _version_ | 1866902202152386560 |
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| author | Rahi, Alireza |
| author_facet | Rahi, Alireza |
| contents | <p> Electrocardiogram (ECG) analysis plays a critical role in the early detection and diagnosis of cardiac abnormalities. In this study, we propose a fusion-based deep learning ensemble framework that integrates two well-established public ECG databases, MIT-BIH Arrhythmia Database and PTB-XL, to develop a robust and automated cardiac diagnostic system. Our framework employs two base deep learning models — a CNN+LSTM hybrid and a DenseNet1D-inspired network — and combines their predictive features through a meta-learner based on Gradient Boosting. This multi-model integration, designed as a "mini doctor for the heart," leverages the complementary strengths of both datasets and models. Experimental results demonstrate that the ensemble achieves near-perfect performance with Accuracy up to 100% and ROC-AUC of 1.000, surpassing the performance of individual models. These findings highlight the potential of database fusion and model ensembling for building reliable and scalable solutions in computer-aided cardiac diagnosis.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17370018 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Cardiac-Diagnosis: First stable release Rahi, Alireza Electrocardiogram (ECG) Cardiac Tamponade/diagnosis Edema, Cardiac/diagnosis Arrhythmias, Cardiac/diagnosis Cardiac Output, Low/diagnosis Cardiac Complexes, Premature/diagnosis Cardiac Papillary Fibroelastoma/diagnosis Cardiac Output, High/diagnosis Sinus Arrest, Cardiac/diagnosis Cardiac Conduction System Disease/diagnosis Post-Cardiac Arrest Syndrome/diagnosis Out-of-Hospital Cardiac Arrest/diagnosis Cardio-Renal Syndrome/diagnosis Arrhythmias, Cardiac/diagnostic imaging Cardiac Tamponade/diagnostic imaging Cardia/diagnostic imaging Edema, Cardiac/diagnostic imaging Sinus Arrest, Cardiac/diagnostic imaging Cardiac Papillary Fibroelastoma/diagnostic imaging Cardiac Output, Low/diagnostic imaging Cardiac Complexes, Premature/diagnostic imaging Cardiac Diagnosis -Gradient Boosting Ensemble Learning <p> Electrocardiogram (ECG) analysis plays a critical role in the early detection and diagnosis of cardiac abnormalities. In this study, we propose a fusion-based deep learning ensemble framework that integrates two well-established public ECG databases, MIT-BIH Arrhythmia Database and PTB-XL, to develop a robust and automated cardiac diagnostic system. Our framework employs two base deep learning models — a CNN+LSTM hybrid and a DenseNet1D-inspired network — and combines their predictive features through a meta-learner based on Gradient Boosting. This multi-model integration, designed as a "mini doctor for the heart," leverages the complementary strengths of both datasets and models. Experimental results demonstrate that the ensemble achieves near-perfect performance with Accuracy up to 100% and ROC-AUC of 1.000, surpassing the performance of individual models. These findings highlight the potential of database fusion and model ensembling for building reliable and scalable solutions in computer-aided cardiac diagnosis.</p> |
| title | Cardiac-Diagnosis: First stable release |
| topic | Electrocardiogram (ECG) Cardiac Tamponade/diagnosis Edema, Cardiac/diagnosis Arrhythmias, Cardiac/diagnosis Cardiac Output, Low/diagnosis Cardiac Complexes, Premature/diagnosis Cardiac Papillary Fibroelastoma/diagnosis Cardiac Output, High/diagnosis Sinus Arrest, Cardiac/diagnosis Cardiac Conduction System Disease/diagnosis Post-Cardiac Arrest Syndrome/diagnosis Out-of-Hospital Cardiac Arrest/diagnosis Cardio-Renal Syndrome/diagnosis Arrhythmias, Cardiac/diagnostic imaging Cardiac Tamponade/diagnostic imaging Cardia/diagnostic imaging Edema, Cardiac/diagnostic imaging Sinus Arrest, Cardiac/diagnostic imaging Cardiac Papillary Fibroelastoma/diagnostic imaging Cardiac Output, Low/diagnostic imaging Cardiac Complexes, Premature/diagnostic imaging Cardiac Diagnosis -Gradient Boosting Ensemble Learning |
| url | https://doi.org/10.5281/zenodo.17370018 |