Modified parametric-based AlexNet structure to classify ECG signals for cardiovascular diseases
Patients with cardiovascular disease typically need constant monitoring, and this is made possible by analyzing their electrocardiogram (ECG) signals to determine the specific pattern of variation associated with each disease. The various malfunctions of the internal parts of the organ generates mul...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-06-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917423001526 |
| _version_ | 1797797159714684928 |
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| author | S.T. Aarthy J.L. Mazher Iqbal |
| author_facet | S.T. Aarthy J.L. Mazher Iqbal |
| author_sort | S.T. Aarthy |
| collection | DOAJ |
| description | Patients with cardiovascular disease typically need constant monitoring, and this is made possible by analyzing their electrocardiogram (ECG) signals to determine the specific pattern of variation associated with each disease. The various malfunctions of the internal parts of the organ generates multiple ECG signals with different commonalities, which can then be used to classify the sub-categories of each disease. In this study, a deep learning network for classifying ECG signals were presented and features were extracted to improve disease classification. Totally 5,655 ECG recordings was used. Initially all 30-s signals were converted to RGB images using Continuous Wavelet Transform (CWT), and then we those images were fed to AlexNet that was trained using slightly modified parameters. According to the results, the proposed method not only outperforms the state-of-the-art methods (98.82% accuracy) but also has higher recall (98.9%) and precision (97.9%) in the aggregate. |
| first_indexed | 2024-03-13T03:43:58Z |
| format | Article |
| id | doaj.art-cb0e0928920346edab2a07e29d3c544a |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-03-13T03:43:58Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-cb0e0928920346edab2a07e29d3c544a2023-06-23T04:44:30ZengElsevierMeasurement: Sensors2665-91742023-06-0127100816Modified parametric-based AlexNet structure to classify ECG signals for cardiovascular diseasesS.T. Aarthy0J.L. Mazher Iqbal1Corresponding author.; Dept. of Electronics and Communication Engineering, Vel Tech Rangarajan Dr.Sagunthala R &D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, IndiaDept. of Electronics and Communication Engineering, Vel Tech Rangarajan Dr.Sagunthala R &D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, IndiaPatients with cardiovascular disease typically need constant monitoring, and this is made possible by analyzing their electrocardiogram (ECG) signals to determine the specific pattern of variation associated with each disease. The various malfunctions of the internal parts of the organ generates multiple ECG signals with different commonalities, which can then be used to classify the sub-categories of each disease. In this study, a deep learning network for classifying ECG signals were presented and features were extracted to improve disease classification. Totally 5,655 ECG recordings was used. Initially all 30-s signals were converted to RGB images using Continuous Wavelet Transform (CWT), and then we those images were fed to AlexNet that was trained using slightly modified parameters. According to the results, the proposed method not only outperforms the state-of-the-art methods (98.82% accuracy) but also has higher recall (98.9%) and precision (97.9%) in the aggregate.http://www.sciencedirect.com/science/article/pii/S2665917423001526Cardiovascular diseaseFeature extractionMultiple ECG signalsKnowledge transferParameter-modified deep learning network |
| spellingShingle | S.T. Aarthy J.L. Mazher Iqbal Modified parametric-based AlexNet structure to classify ECG signals for cardiovascular diseases Measurement: Sensors Cardiovascular disease Feature extraction Multiple ECG signals Knowledge transfer Parameter-modified deep learning network |
| title | Modified parametric-based AlexNet structure to classify ECG signals for cardiovascular diseases |
| title_full | Modified parametric-based AlexNet structure to classify ECG signals for cardiovascular diseases |
| title_fullStr | Modified parametric-based AlexNet structure to classify ECG signals for cardiovascular diseases |
| title_full_unstemmed | Modified parametric-based AlexNet structure to classify ECG signals for cardiovascular diseases |
| title_short | Modified parametric-based AlexNet structure to classify ECG signals for cardiovascular diseases |
| title_sort | modified parametric based alexnet structure to classify ecg signals for cardiovascular diseases |
| topic | Cardiovascular disease Feature extraction Multiple ECG signals Knowledge transfer Parameter-modified deep learning network |
| url | http://www.sciencedirect.com/science/article/pii/S2665917423001526 |
| work_keys_str_mv | AT staarthy modifiedparametricbasedalexnetstructuretoclassifyecgsignalsforcardiovasculardiseases AT jlmazheriqbal modifiedparametricbasedalexnetstructuretoclassifyecgsignalsforcardiovasculardiseases |