Key result
Modified vgg16 deep learning model predicts cardiac arrhythmia from ECG signals with ~99.8% accuracy.
Why the study?
Arrhythmia requires precise and fast diagnosis, motivating automated irregular heartbeat prediction using ECG signals to accurately determine arrhythmia class with less data loss.
Can deep learning models accurately predict and classify cardiac arrhythmias from ECG signals?
Can deep learning models accurately predict and classify cardiac arrhythmias from ECG signals?
Deep learning models, particularly vgg16, can achieve highly accurate automated classification of cardiac arrhythmias from ECG signals.
Supports automated ECG arrhythmia detection clinically; extends VGG16 applications to cardiac signal classification.
Abnormal heart rhythm or irregular heartbeat, often known as arrhythmia. It is a kind of cardiovascular illness that necessitates a precise and fast diagnosis. Because of its simplicity and non-invasive nature, an electrocardiogram (ECG) that detects the electric activity of the heart has been frequently used to identify cardiac disorders. Each heartbeat's electrical signal, the peak of action impulse waveforms produced by various specialised cardiac tissues, can be used to diagnose various heart defects. Deep learning has evolved as a significant technique in recent decades due to its ability to handle vast amounts of data. The use of hidden layers in the convolution layer have enhanced pattern recognition performance. Deep learning has aided in the automation of medical image analysis and can aid in detecting of any anomalies in the medical images. In this work, ECG-based automated irregular heartbeat prediction is conducted to determine to which arrhythmia class it belongs with greater accuracy and less data loss. This study is based on convolutional neural networks, which are used to evaluate ECG images. For the normal case and cases impacted by various arrhythmias and my-ocardial infarction, the signals correspond to electrocardiogram (ECG) forms of heartbeats.lD-CNN, ResNet34, ResNet50, vgg16, and vgg19 models are utilised to predict of cardiac arrhythmia. vgg16 performed the best and is chosen to be further tweaked to improve accuracy to 99.79 percent.
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Padhy et al. (2023) studied Cardiac Arrhythmia. vgg16 deep learning model vs. 1D-CNN, ResNet34, ResNet50, and vgg19 models was evaluated on Prediction accuracy of cardiac arrhythmia. A tweaked vgg16 deep learning model predicted cardiac arrhythmia from ECG signals with an accuracy of 99.79%.
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