Deep Learning ECG Segmentation for Atrial Fibrillation Detection

The patent replaces conventional rule-based algorithms with a deep learning-based automated algorithm. The deep learning model automatically learns complex patterns in ECG waves without requiring manual rule design, thereby improving detection accuracy while handling the diversity of arrhythmia waves. The system uses trained neural networks to classify arrhythmia types based on input ECG signals.

The patent divides the ECG signal into multiple segments or windows for parallel processing. By segmenting the continuous ECG signal into smaller chunks, the deep learning model can process each segment independently and more efficiently, reducing overall processing time while maintaining high accuracy. This enables real-time or near-real-time arrhythmia detection.

The patent implements an automated deep learning-based ECG analysis system that operates independently without requiring continuous manual intervention by medical staff. The system automatically acquires ECG signals, processes them through the deep learning model, and generates arrhythmia detection results, enabling continuous monitoring with minimal human resources.

The patent enhances the one-dimensional ECG signal visualization by adding temporal and contextual dimensions. The system displays ECG waves with time stamps, segmentation markers, and arrhythmia detection results overlaid on the waveform. This multi-dimensional presentation improves readability and interpretability during real-time monitoring while maintaining efficient data processing.