A proposed small CNN for arrhythmia detection using ECG signals demonstrated promising potential when compared against pretrained models like GoogLeNet.
Does a small CNN improve arrhythmia detection performance compared to pretrained models like GoogLeNet using ECG signals?
A proposed small CNN demonstrates promising potential for arrhythmia detection from ECG signals when compared to pretrained models.
Heart diseases is the world's principal cause of death, and arrhythmia poses a serious risk to the health of the patient. Electrocardiogram (ECG) signals can be used to detect arrhythmia early and accurately, which is essential for immediate treatment and intervention. Deep learning approaches have played an important role in automatically identifying complicated patterns from ECG data, which can be further used to identify arrhythmia. In this paper, deep-learning-based methods for arrhythmia identification using ECG signals are thoroughly studied and their performances evaluated on the basis of accuracy, specificity, precision, and F1 score. We propose the development of a small CNN, and its performance is compared against pretrained models like GoogLeNet. The comparative study demonstrates the promising potential of deep-learning-based arrhythmia identification using ECG signals.
Katal et al. (Tue,) conducted a other in Arrhythmia. Small CNN for arrhythmia detection vs. Pretrained models like GoogLeNet was evaluated on Performance evaluated on accuracy, specificity, precision, and F1 score. A proposed small CNN for arrhythmia detection using ECG signals demonstrated promising potential when compared against pretrained models like GoogLeNet.