Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification

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.

ECG-Based Cardiac Disease Detection in Time-Frequency Domain using Grid Search Optimized Wavelet Transforms

“…The WOA-PNN approach intelligently optimizes hyperparameters, achieving high identification accuracy rates of 96.97% for a single ECG cycle and 99.43% for three cycles across the ECG-ID, MIT-BIH Normal Sinus Rhythm, and MIT-BIH Arrhythmia databases [11]. In 2022, Hammad et al [12] have shown that multimodal approaches can significantly improve classification accuracy and efficiency in detecting arrhythmias compared to single models. Deriche et al [13] proposed a study employing 13 ECG geometric features, based on the Pan-Tompkins QRS model, to classify five abnormal heartbeat types using the MIT-BIH arrhythmia database.…”

An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection

“…Originally introduced in 1990 for the recognition of handwritten digits, researchers are currently incorporating artificial intelligence powered Computer-Aided Diagnosis (CAD) systems into medical imaging to facilitate early diagnosis and intervention by physicians [ 12 ]. CNNs have recently demonstrated their potential in accurately classifying various types of arrhythmias and ECG signals [ [13] , [14] , [15] , [16] ]. The utilization of CNN architecture enables the automatic extraction of ECG signal features in a non-linear manner, providing an objective approach for detecting and classifying heart rhythms, even those that present challenges or might lead to misjudgments when using traditional methods [ 17 ].…”

Accurate Detection of Alzheimer’s Disease Using Lightweight Deep Learning Model on MRI Data

“…Research is ongoing to develop more effective treatments and to better understand the underlying causes of AD [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. In recent years, deep learning techniques have been successfully applied to a wide range of medical imaging tasks [ 24 , 25 , 26 , 27 , 28 ], including the detection of AD [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. These techniques have the ability to automatically learn and extract features from large datasets, making them well suited for the analysis of complex medical images.…”