Automated Arrhythmia Detection from ECG Signals: AComparative Study of Bidirectional Long Short-Term MemoryNetworks and Residual Convolutional Neural Networks
This study benchmarks Bidirectional Long Short-Term Memory networks with attention against Residual Convolutional Neural Networks for automated ECG arrhythmia detection, finding that the ResNet architecture achieves superior performance (98.0% accuracy) by better extracting complex morphological features from a dataset of over 109,000 heartbeats.