Deep Neural Network for Arrhythmia Detection via Spectrum Images
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Solution Overview
Problem
Current systems for interpreting electrocardiogram (ECG) waveforms require feature extraction and pre-processing, which can lead to inaccuracies in arrhythmia detection.
Innovation Solution
A deep neural network system that minimally processes cardiac waveforms to transform them into spectrum images, using a representation neural network to generate a latent representation and a classifier neural network to identify arrhythmias without the need for feature extraction or extensive pre-processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional feature extraction algorithms are used to process ECG waveforms, then the system can identify predefined waveform features, but the accuracy of arrhythmia detection deteriorates due to processing errors and loss of original signal characteristics
Solution Approach 1:
The patent removes the traditional feature extraction step entirely from the processing pipeline. Instead of extracting predefined features from ECG waveforms, the raw waveform data is directly fed into a deep neural network that automatically learns relevant features, thereby eliminating information loss associated with manual feature extraction while maintaining detection accuracy
Solution Approach 2:
The patent replaces the mechanical feature extraction system (manual algorithm-based feature identification) with an intelligent deep learning system. The neural network automatically performs feature learning and arrhythmia classification, substituting the traditional mechanical processing approach with a more sophisticated computational model that preserves original signal characteristics
2Productivity
If extensive pre-processing and feature extraction are performed on ECG waveforms, then the data can be structured for analysis, but the processing time increases and detection speed decreases
Solution Approach 1:
The patent performs preliminary action by pre-training the deep neural network on large datasets of ECG waveforms before deployment. This pre-training enables the model to quickly process new waveforms in real-time without requiring extensive pre-processing steps during actual detection, thereby reducing processing time while maintaining high detection speed
Solution Approach 2:
The patent removes the time-consuming feature extraction and extensive pre-processing steps from the detection pipeline. By directly inputting raw ECG waveforms into the pre-trained neural network, the system eliminates redundant processing operations and achieves faster arrhythmia detection
3Reliability
If traditional interpretation systems are used, then they can provide suggested interpretations based on processed ECG data, but they require complex feature extraction algorithms that reduce reliability
Solution Approach 1:
The patent implements self-service by enabling the deep neural network to automatically perform both feature learning and arrhythmia classification without relying on external feature extraction algorithms. The system serves itself by integrating multiple functions into a unified model, reducing complexity while improving reliability through consistent automated processing
Solution Approach 2:
The patent merges the feature extraction function and the classification function into a single integrated deep neural network. This consolidation eliminates the need for separate feature extraction algorithms and reduces system complexity, while the unified model improves reliability by maintaining consistent processing throughout the entire analysis pipeline
Data Source
AI summary
A system for identifying arrhythmias based on cardiac waveforms includes a storage system storing a trained deep neural network system, wherein the trained deep neural system includes a trained representation neural network and a trained classifier neural network. A processing system is communicatively connected to the storage system and configured to receive cardiac waveform data for a patient, identify a time segment in the cardiac waveform data, and transform the time segment into a spectrum image. The processing system is further configured to generate, with the representation neural network, a latent representation from the spectrum image, and then to generate, with the classifier neural network, an arrhythmia classifier from the latent representation.