Neural Network Arrhythmia Detection Using Coherent Mapping
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Solution Overview
Problem
Current medical procedures for diagnosing and treating cardiac arrhythmias, such as atrial fibrillation, rely on time-consuming and resource-intensive visualization and mapping techniques, often resulting in human error due to the complexity of interpreting electrocardiogram (ECG) data.
Innovation Solution
A system utilizing a neural network trained on historical ECG data to predict arrhythmia locations, incorporating body surface electrodes and a processor that generates models for automatic detection and coherent mapping adjustments, reducing the need for manual identification and minimizing human error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional visualization and mapping techniques (Fluoroscopies, CT, MRI) are used to map intra-body surfaces, then mapping accuracy is improved, but the time and resources required increase significantly
Solution Approach 1:
The patent replaces complex mechanical imaging systems (Fluoroscopies, CT, MRI) with an electrical field-based mapping system that uses body surface electrodes to detect electrocardiogram data. This substitution enables rapid, non-invasive mapping of cardiac electrical activity without the time-consuming and resource-intensive procedures of traditional imaging methods.
Solution Approach 2:
The patent introduces a neural network model as an intermediary between raw ECG data and arrhythmia location identification. This intermediary processes and interprets the electrical signals automatically, eliminating the need for manual analysis by medical professionals and providing rapid, accurate results without requiring traditional imaging infrastructure.
2Measurement precision
If manual interpretation of ECG data by medical professionals is used to locate arrhythmia sites, then diagnostic accuracy can be achieved, but human error and extended analysis time occur
Solution Approach 1:
The patent implements a self-service diagnostic system where the neural network model automatically analyzes ECG data and identifies arrhythmia locations without requiring manual interpretation by medical professionals. The system trains itself on historical data and autonomously provides diagnostic results, eliminating human error while maintaining high accuracy.
Solution Approach 2:
The patent incorporates a feedback mechanism where the neural network model is trained on historical ECG data with known arrhythmia locations. This feedback loop continuously improves the model's accuracy by learning from past cases, enabling reliable automated diagnosis that surpasses manual interpretation consistency.
3Loss of information
If comprehensive visualization and mapping procedures are performed, then complete anatomical understanding is achieved, but procedural complexity and resource consumption increase
Solution Approach 1:
The patent extracts only the essential electrical activity information from the heart by placing electrodes on the body surface, rather than performing comprehensive anatomical imaging. This extraction approach captures the critical data needed for arrhythmia localization while eliminating the complexity of full anatomical mapping procedures.
Data Source
AI summary
Systems, devices, and techniques are disclosed for automatically detecting arrhythmia locations. The systems, devices, and techniques include a plurality of body surface electrodes configured to sense electrocardiogram (ECG) data. The systems, devices, and techniques include a processor including a neural network configured to receive a plurality of historical ECG data and corresponding arrhythmia locations determined based on each of the plurality of historical ECG data, train a learning system based on the plurality of historical ECG data and corresponding arrhythmia locations, generate a model based on the learning system. New ECG data may be received from the plurality of body surface electrodes and the processor may provide a new arrhythmia location based on the new ECG data. Additionally, a new coherent mapping adjustment may be provided based on a model that is trained using historical coherent mapping adjustments.