Szczegóły publikacji
Opis bibliograficzny
A hybrid approach of a deep learning technique for real–time ECG beat detection / Kiran Kumar Patro, Allam Jaya Prakash, Saunak Samantray, Joanna Pławiak, Ryszard TADEUSIEWICZ, Paweł Pławiak // International Journal of Applied Mathematics and Computer Science ; ISSN 1641-876X. — 2022 — vol. 32 no. 3, s. 455–465. — Bibliogr. s. 461–464, Abstr.
Autorzy (6)
- Patro Kiran Kumar
- Prakash Allam Jaya
- Samantray Saunak
- Pławiak Joanna
- AGHTadeusiewicz Ryszard
- Pławiak Paweł
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 142377 |
|---|---|
| Data dodania do BaDAP | 2022-09-22 |
| Tekst źródłowy | URL |
| DOI | 10.34768/amcs-2022-0033 |
| Rok publikacji | 2022 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | International Journal of Applied Mathematics and Computer Science |
Abstract
This paper presents a new customized hybrid approach for early detection of cardiac abnormalities using an electrocardiogram (ECG). The ECG is a bio-electrical signal that helps monitor the heart’s electrical activity. It can provide health information about the normal and abnormal physiology of the heart. Early diagnosis of cardiac abnormalities is critical for cardiac patients to avoid stroke or sudden cardiac death. The main aim of this paper is to detect crucial beats that can damage the functioning of the heart. Initially, a modified Pan–Tompkins algorithm identifies the characteristic points, followed by heartbeat segmentation. Subsequently, a different hybrid deep convolutional neural network (CNN) is proposed to experiment on standard and real-time long-term ECG databases. This work successfully classifies several cardiac beat abnormalities such as supra-ventricular ectopic beats (SVE), ventricular beats (VE), intra-ventricular conduction disturbances beats (IVCD), and normal beats (N). The obtained classification results show a better accuracy of 99.28% with an F1 score of 99.24% with the MIT–BIH database and a descent accuracy of 99.12% with the real-time acquired database.
