Classification of pathological ECG beats based on wireless body sensor networks and fractional Fourier transform and convolutional neural network.
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| Title: | Classification of pathological ECG beats based on wireless body sensor networks and fractional Fourier transform and convolutional neural network. |
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| Authors: | Chaabane, Mohamed1 (AUTHOR), Chehri, Abdellah2 (AUTHOR), Saadane, Rachid3 (AUTHOR), Jeon, Gwanggil4 (AUTHOR) gjeon@inu.ac.kr, El Rharras, Abdessamad1 (AUTHOR) |
| Source: | Wireless Networks (10220038). Nov2024, Vol. 30 Issue 8, p7059-7073. 15p. |
| Subjects: | Convolutional neural networks, Body sensor networks, Body area networks, Wireless sensor networks, Artificial intelligence |
| Abstract: | The heart that beats with an irregular rhythm, sometimes too fast, quickly gets tired. Even though it is the most common cardiac abnormality, atrial fibrillation remains very complex to diagnose. The electrocardiogram (ECG) contains much information about the functioning and possible pathologies of the heart. Artificial intelligence is reshaping healthcare and can be used to diagnose health abnormalities or predict events. The paper aims to combine advanced ECG signal processing with a deep learning technique. After using several filtering algorithms, we divide the ECG signal and then convert it into the frequency domain, precisely the fractional Fourier transform, then into small equal parts so that each contains the typical features of the signal. We collect them in the form of simple arrays. The next step is to convert these arrays into images, which involves transforming each one-dimensional ECG beat into a two-dimensional image. We have relied on this MIT-BIH database research. In the classification phase, 80% was used as training data and 20% for validation and testing. In the end, we classified five categories of heart disease. The proposed technique shows promising results, compared to other similar works, with an accuracy rate of 99.34%. [ABSTRACT FROM AUTHOR] |
| Copyright of Wireless Networks (10220038) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 180904887 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Classification of pathological ECG beats based on wireless body sensor networks and fractional Fourier transform and convolutional neural network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chaabane%2C+Mohamed%22">Chaabane, Mohamed</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chehri%2C+Abdellah%22">Chehri, Abdellah</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Saadane%2C+Rachid%22">Saadane, Rachid</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jeon%2C+Gwanggil%22">Jeon, Gwanggil</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> gjeon@inu.ac.kr</i><br /><searchLink fieldCode="AR" term="%22El+Rharras%2C+Abdessamad%22">El Rharras, Abdessamad</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Wireless+Networks+%2810220038%29%22">Wireless Networks (10220038)</searchLink>. Nov2024, Vol. 30 Issue 8, p7059-7073. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Body+sensor+networks%22">Body sensor networks</searchLink><br /><searchLink fieldCode="DE" term="%22Body+area+networks%22">Body area networks</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+sensor+networks%22">Wireless sensor networks</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The heart that beats with an irregular rhythm, sometimes too fast, quickly gets tired. Even though it is the most common cardiac abnormality, atrial fibrillation remains very complex to diagnose. The electrocardiogram (ECG) contains much information about the functioning and possible pathologies of the heart. Artificial intelligence is reshaping healthcare and can be used to diagnose health abnormalities or predict events. The paper aims to combine advanced ECG signal processing with a deep learning technique. After using several filtering algorithms, we divide the ECG signal and then convert it into the frequency domain, precisely the fractional Fourier transform, then into small equal parts so that each contains the typical features of the signal. We collect them in the form of simple arrays. The next step is to convert these arrays into images, which involves transforming each one-dimensional ECG beat into a two-dimensional image. We have relied on this MIT-BIH database research. In the classification phase, 80% was used as training data and 20% for validation and testing. In the end, we classified five categories of heart disease. The proposed technique shows promising results, compared to other similar works, with an accuracy rate of 99.34%. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Wireless Networks (10220038) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11276-023-03566-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 7059 Subjects: – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Body sensor networks Type: general – SubjectFull: Body area networks Type: general – SubjectFull: Wireless sensor networks Type: general – SubjectFull: Artificial intelligence Type: general Titles: – TitleFull: Classification of pathological ECG beats based on wireless body sensor networks and fractional Fourier transform and convolutional neural network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chaabane, Mohamed – PersonEntity: Name: NameFull: Chehri, Abdellah – PersonEntity: Name: NameFull: Saadane, Rachid – PersonEntity: Name: NameFull: Jeon, Gwanggil – PersonEntity: Name: NameFull: El Rharras, Abdessamad IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 10220038 Numbering: – Type: volume Value: 30 – Type: issue Value: 8 Titles: – TitleFull: Wireless Networks (10220038) Type: main |
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