Automatic diagnosis of arrhythmia by electrocardiogram has a significant role to play in preventing and detecting cardiovascular disease at an early stage. In this study, a deep neural network model based on Harris hawks optimization is presented to arrive at a temporal and spatial fusion of information from ECG signals. Compared with the initial model of the multichannel deep neural network mechanism, the proposed model of this research has a flexible input length; the number of parameters is halved and it has a more than 50% reduction in computations in real-time processing. The results of the simulation demonstrate that the approach proposed in this research had a rate of 96.04%, 93.94%, and 95.00% for sensitivity, specificity, and accuracy. Furthermore, the proposed approach has a practical advantage over other similar previous methods.
Eser Adı (dc.title) | An Approach for Cardiac Coronary Detection of Heart Signal Based on Harris Hawks Optimization and Multichannel Deep Convolutional Learning |
Yazar (dc.contributor.author) | Cevat Rahebi |
Yayın Yılı (dc.date.issued) | 2022 |
Tür (dc.type) | Makale |
Özet (dc.description.abstract) | Automatic diagnosis of arrhythmia by electrocardiogram has a significant role to play in preventing and detecting cardiovascular disease at an early stage. In this study, a deep neural network model based on Harris hawks optimization is presented to arrive at a temporal and spatial fusion of information from ECG signals. Compared with the initial model of the multichannel deep neural network mechanism, the proposed model of this research has a flexible input length; the number of parameters is halved and it has a more than 50% reduction in computations in real-time processing. The results of the simulation demonstrate that the approach proposed in this research had a rate of 96.04%, 93.94%, and 95.00% for sensitivity, specificity, and accuracy. Furthermore, the proposed approach has a practical advantage over other similar previous methods. |
Açık Erişim Tarihi (dc.date.available) | 2022-07-30 |
Yayıncı (dc.publisher) | Hindawi Computational Intelligence and Neuroscience |
Dil (dc.language.iso) | En |
Konu Başlıkları (dc.subject) | Automatic diagnosis, cardiovascular, ECG. |
Tek Biçim Adres (dc.identifier.uri) | https://hdl.handle.net/20.500.14081/1625 |
ISSN (dc.identifier.issn) | 1687-5265 |
Dergi (dc.relation.journal) | Computational Intelligence And Neuroscience |
Dergi Sayısı (dc.identifier.issue) | 1 |
Esere Katkı Sağlayan (dc.contributor.other) | Rahebi, Javad |
Esere Katkı Sağlayan (dc.contributor.other) | Munilla, Jorge |
Esere Katkı Sağlayan (dc.contributor.other) | Alsafi, Haedar |
DOI (dc.identifier.doi) | 10.1155/2022/7276028 |
Orcid (dc.identifier.orcid) | 0000000198754860 |
Dergi Cilt (dc.identifier.volume) | 2022 |
wosquality (dc.identifier.wosquality) | Q2 |
wosauthorid (dc.contributor.wosauthorid) | DNF-7937-2022 |
Department (dc.contributor.department) | Yazılım Mühendisliği |
Wos No (dc.identifier.wos) | WOS:000855557000023 |
Veritabanları (dc.source.platform) | Wos |
Veritabanları (dc.source.platform) | Scopus |
Veritabanları (dc.source.platform) | PubMed |