ECG Identity Analysis via Machine Learning
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Solution Overview
Problem
Existing methods for analyzing electrocardiograms (ECGs) are prone to human error, leading to potential unnoticed changes in ECG morphology, which may indicate underlying medical conditions.
Innovation Solution
A machine learning-based identity analysis system that uses ECG data to identify individuals and detect subtle changes in ECG morphology over time, employing techniques such as neural networks and auto-encoders to process and analyze ECG signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If visual analysis of electrocardiograms by healthcare providers is used, then the system is simple and easy to operate, but human error leads to unnoticed changes in ECG morphology
Solution Approach 1:
The patent replaces the mechanical/visual inspection system with an automated machine learning-based image processing system. The trained machine learning model automatically analyzes ECG morphology images, substituting human visual inspection with algorithmic processing to eliminate human error while maintaining operational simplicity through automated workflows.
Solution Approach 2:
The patent introduces an intermediary automated analysis system between the ECG recording and the final diagnosis. The machine learning model acts as a mediator that processes ECG images, identifies morphological changes, and provides objective measurements, bridging the gap between raw data and clinical interpretation.
2Measurement precision
If automated machine learning analysis is implemented, then detection precision of ECG morphology changes improves, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on large datasets of ECG images before deployment. The models are trained in advance to recognize normal and abnormal ECG morphologies, so that during actual use, the complex analysis work has already been prepared, enabling precise detection without requiring complex real-time processing infrastructure.
Solution Approach 2:
The patent uses copying by creating trained machine learning model copies that can be deployed across multiple systems. Once a model is trained to achieve high detection precision, identical copies can be used throughout the healthcare system, maintaining precision while reducing the complexity burden on individual devices since the intelligence is replicated rather than regenerated locally.
Data Source
AI summary
A set of training electrocardiograms (ECGs) for each of a plurality of subjects is processed using a machine learning model to generate an output for each training ECG of each of the plurality of subjects. Training ECGs for each subject are labeled with an identity of the subject. A machine learning model is trained by comparing the output generated for each training ECG to a corresponding label of the training ECG to generate an identity model to identify ECGs of a first subject of the plurality of subjects. A first ECG is received from an ECG sensor and input to the identity model, which generates an output indicating whether the first ECG corresponds to the first subject. In response to the output indicating that the first ECG does not correspond to the first subject, a condition that the first subject has or may develop is determined based on the output.