Efficient lightweight multimodel deep fusion based on ECG for arrhythmia classification
Authors:
- Mohamed Hammad,
- Souham Meshoul,
- Piotr Dziwiński,
- Paweł Pławiak,
- Ibrahim A. Elgendy
Abstract
An arrhythmia happens when the electrical signals that organize the heartbeat do not work accurately. Most cases of arrhythmias may increase the risk of stroke or cardiac arrest. As a result, early detection of arrhythmia reduces fatality rates. This research aims to provide a lightweight multimodel based on convolutional neural networks (CNNs) that can transfer knowledge from many lightweight deep learning models and decant it into one model to aid in the diagnosis of arrhythmia by using electrocardiogram (ECG) signals. Thus, we gained a multimodel able to classify arrhythmia from ECG signals. Our system’s effectiveness is examined by using a publicly accessible database and a comparison to the current methodologies for arrhythmia classification. The results we achieved by using our multimodel are better than those obtained by using a single model and better than most of the previous detection methods. It is worth mentioning that this model produced accurate classification results on small collection of data. Experts in this field can use this model as a guide to help them make decisions and save time.
- Record ID
- CUT89308f1cfe704d9491ab9b1314e9f9a2
- Publication categories
- ;
- Author
- Journal series
- Sensors, ISSN , e-ISSN 1424-8220, Biweekly
- Issue year
- 2022
- Vol
- 22
- No
- 23
- Pages
- [1-14]
- Article number
- 9347
- Other elements of collation
- schem.; tab.; wykr.; Bibliografia (na s.) - 13-14; Bibliografia (liczba pozycji) - 38; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 22, Iss. 23
- Substantive notes
- Special Issue: Sensors and Signal Processing for Biomedical Application
- Keywords in English
- arrhythmia, CNN, ECG, lightweight, multimodel, fusion
- ASJC Classification
- ; ; ;
- DOI
- DOI:10.3390/s22239347 Opening in a new tab
- URL
- https://www.mdpi.com/1424-8220/22/23/9347 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 100
- Score source
- journalList
- Score
- Publication indicators
- Citation count
- 9
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT89308f1cfe704d9491ab9b1314e9f9a2/
- URN
urn:pkr-prod:CUT89308f1cfe704d9491ab9b1314e9f9a2
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.