Correlation Image Arrhythmia Classification Circuit
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Solution Overview
Problem
Current medical devices face challenges in accurately detecting and classifying cardiac arrhythmias due to noise and interference in physiologic signals, leading to inappropriate therapies and increased costs, as conventional heart rate-based or morphology-based methods are prone to false positives and misclassification.
Innovation Solution
The system employs a correlator circuit to generate autocorrelation sequences and correlation images from cardiac activity, which are then classified by an arrhythmia classifier circuit to accurately identify arrhythmia types, reducing noise susceptibility and improving discrimination between arrhythmia types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional heart rate-based or morphology-based methods are used for arrhythmia detection, then the system is simpler to implement, but the accuracy and reliability of arrhythmia classification deteriorates due to noise and interference in physiologic signals
Solution Approach 1:
The patent replaces conventional mechanical signal processing methods with correlation image analysis. Instead of using traditional heart rate-based or morphology-based detection methods that are susceptible to noise, the system generates correlation images by stacking autocorrelation sequences, transforming the signal processing approach to achieve superior noise resistance and classification accuracy.
Solution Approach 2:
The patent introduces a new dimensional approach by creating correlation images that stack autocorrelation sequences across multiple time lags. This transforms one-dimensional signal data into a two-dimensional correlation image space, enabling pattern recognition that is resistant to noise and interference while maintaining computational efficiency.
2Reliability
If conventional arrhythmia detection methods are used, then the system has lower computational requirements, but false positives and misclassification increase leading to inappropriate therapies
Solution Approach 1:
The patent performs preliminary signal processing by generating autocorrelation sequences for multiple time lags before creating the final correlation image. This preliminary action organizes the signal data in a way that enhances pattern recognition capabilities, allowing for more accurate arrhythmia detection with reduced false positives and misclassification.
3Reliability
If noise-resistant correlation image analysis is implemented, then arrhythmia classification accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent segments the cardiac signal into multiple time-lagged segments and generates autocorrelation sequences for each segment. By dividing the complex signal processing task into manageable segments, the system achieves noise resistance through correlation image analysis while keeping computational complexity manageable through systematic processing of individual segments.
Data Source
AI summary
Systems and methods for classifying a cardiac arrhythmia are discussed. An exemplary system includes a correlator circuit to generate autocorrelation sequences using information of cardiac activity of a subject, including signal segments taken from a cardiac signal at respective elapsed time with respect to reference time. The correlator circuit can generate a correlation image using the autocorrelation sequences. The correlation image may be constructed by stacking the autocorrelation sequences according to the elapsed time of signal segments. An arrhythmia classifier circuit can classify the cardiac activity of the subject as one of arrhythmia types using the correlation image.