Heart Beat Arrhythmia Classification via Noise-Resistant Neural Networks
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Solution Overview
Problem
Conventional correlation algorithms used in electrophysiology procedures fail to accurately and consistently measure the strength of association between heart beats due to noise interference and artifacts, leading to inaccurate identification of arrhythmias.
Innovation Solution
A machine learning system and method utilizing a determination engine that receives pairs of heart beats, generates a model to ignore noise and artifacts, and determines whether two heart beats belong to the same arrhythmia, employing neural networks to improve diagnosis accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional correlation algorithms are used to identify arrhythmias, then the measurement process is simple, but the measurement precision and reliability deteriorate due to noise interference and artifacts
Solution Approach 1:
The patent segments the heart beat signal analysis into multiple independent features (morphology, timing intervals, amplitude characteristics) that are evaluated separately. This segmentation allows the system to identify arrhythmias based on multiple criteria rather than a single correlation metric, improving precision while keeping each individual feature extraction relatively simple
Solution Approach 2:
The patent transitions from single-dimensional correlation analysis to multi-dimensional signal characterization by examining heart beats across multiple features simultaneously (temporal, spectral, morphological dimensions). This dimensional expansion enables more accurate arrhythmia detection by capturing complex patterns that single-dimensional analysis misses
2Reliability
If conventional correlation algorithms are used, then the computational process is fast, but the reliability of arrhythmia identification deteriorates due to false correlations from noise and artifacts
Solution Approach 1:
The patent applies preliminary filtering and preprocessing steps to remove obvious noise and artifacts before the main analysis. By preparing the signal in advance (removing power line interference, baseline wander, and gross artifacts), the system reduces false correlations and improves reliability without requiring complex real-time processing during the actual arrhythmia detection
Solution Approach 2:
The patent replaces the mechanical correlation algorithm with an intelligent pattern recognition system that uses machine learning models. This substitution allows the system to learn from training data what constitutes true arrhythmia patterns versus noise, achieving higher reliability while maintaining efficient processing through optimized neural network inference
3Measurement precision
If noise and artifacts are ignored in analysis, then the processing is simpler, but the measurement precision deteriorates because noise can imitate true arrhythmia correlations
Solution Approach 1:
The patent converts the harmful effect of noise and artifacts into a beneficial training opportunity for the machine learning model. By exposing the model to labeled examples of both true arrhythmias and noise artifacts during training, the system learns to distinguish between them. The noise that would normally degrade performance becomes part of the training data that improves the model's robustness and precision
Data Source
AI summary
A method that is executed by a determination engine is provided. The method includes receiving one or more pairs of heart beats, generating a model based on the one or more pairs of heart beats, and determining whether two given heart beats are part of a same arrythmia to produce a similarity result for algorithmic input.