Key result
A lightweight multimodel based on convolutional neural networks improved arrhythmia classification from ECG signals compared to single models and previous detection methods.
Why the study?
Arrhythmias increase the risk of stroke or cardiac arrest, making early detection critical to reducing fatality rates.
Does a lightweight multimodel based on CNNs improve arrhythmia classification accuracy from ECG signals compared to single models and previous methods?
Does a lightweight multimodel based on CNNs improve arrhythmia classification accuracy from ECG signals compared to single models and previous methods?
A novel lightweight CNN-based multimodel improves the accuracy of arrhythmia classification from ECG signals, potentially aiding in early diagnosis.
May support accessible arrhythmia detection; leaves open prospective clinical validation before practice change.
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.
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Hammad et al. (2022) studied Arrhythmia. Lightweight multimodel based on convolutional neural networks (CNNs) vs. Single model and previous detection methods was evaluated on Arrhythmia classification. A lightweight multimodel based on convolutional neural networks improved arrhythmia classification from ECG signals compared to single models and previous detection methods.
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