A convolutional neural network integrated into an Android application achieved 99.25% accuracy in classifying ECG beats into five categories, outperforming hand-crafted feature approaches.
Does a CNN-based mobile application improve the accuracy and speed of ECG beat classification compared to manual or hand-crafted feature approaches?
A novel CNN-based mobile application demonstrates high accuracy (99.25%) and rapid processing for automated ECG beat classification, offering potential utility for clinical diagnostics.
The electrocardiogram (ECG) is a valuable tool for diagnosing cardiovascular issues. However, manual analysis can be time-consuming and prone to error. This work presents a novel ECG classification system utilizing a convolutional neural network (CNN) to automatically categorize ECG signals into five classes: normal, left/right bundle branch block, atrial premature contraction, and ventricular premature contraction. Our method extracts nonlinear features directly from the signal, outperforming approaches reliant on hand-crafted features. We achieved 99.25% accuracy on the MIT-BIH database, with rapid classification time (0.0738 seconds per beat). Crucially, we integrated this model into an Android application, enabling convenient ECG signal classification and result display for potential clinical use.
Tummalapalli et al. (Thu,) conducted a other in Cardiovascular issues. Convolutional neural network (CNN) integrated into an Android application vs. Approaches reliant on hand-crafted features was evaluated on Classification accuracy. A convolutional neural network integrated into an Android application achieved 99.25% accuracy in classifying ECG beats into five categories, outperforming hand-crafted feature approaches.