ECG Delineation Using Neural Networks for Hidden P-Wave Detection

The patent replaces handcrafted feature extraction methods with deep learning neural networks that automatically learn features from raw ECG signals. This substitution transitions from manual feature engineering to automated feature learning, resolving the contradiction by achieving both high accuracy through automatic feature extraction and maintaining implementation feasibility through standardized deep learning frameworks.

The patent changes the parameter representation from handcrafted features to learned features through neural network training. By adjusting the learning parameters and network architecture, the system achieves superior detection accuracy while the automated nature of parameter learning simplifies the overall implementation process compared to manual feature engineering.

The patent introduces a new dimension of analysis by using neural networks that process ECG signals in a high-dimensional feature space. This allows simultaneous detection of multiple P-waves and hidden P-waves across different temporal and morphological dimensions, achieving both fast processing through efficient network inference and high accuracy in identifying complex wave patterns.

The neural network model performs multiple functions simultaneously: it detects QRS complexes, identifies P-waves including hidden and multiple P-waves, and characterizes wave morphology all within a single processing framework. This multi-functionality maintains processing speed while dramatically improving detection accuracy across various wave types.

The patent introduces a cloud-based processing platform as an intermediary between ECG device manufacturers and healthcare providers. This intermediary hosts pre-trained neural network models that can be deployed to various devices, achieving high accuracy without requiring each device to have complex local processing capabilities, thus managing system complexity through centralized model management.

The patent uses cloud-based platforms to store and distribute copies of trained neural network models to multiple ECG devices. This allows accurate analysis across numerous devices without each device needing to independently train models, reducing individual device complexity while maintaining high analysis accuracy through shared model resources.