ECG Signal Segmentation for Ischemia Diagnosis
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Solution Overview
Problem
Current methods for detecting myocardial ischemia through electrocardiogram (ECG) analysis, particularly energy spectral density (ESD) analysis, face challenges in interpreting and correlating morphological data due to harmonic disintegration caused by aperiodicities, leading to low accuracy in diagnosing heart diseases.
Innovation Solution
The method involves creating a prime ECG by isolating characteristic segments or windows from raw ECG data, removing extraneous information, and calculating distribution functions to accurately diagnose heart diseases like myocardial ischemia by addressing harmonic disintegration through signal processing techniques such as discrete Fourier transform and averaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If energy spectral density (ESD) analysis is used to detect myocardial ischemia, then the method provides a non-invasive and time-efficient observation, but the accuracy in diagnosing heart diseases is low due to harmonic disintegration caused by aperiodicities
Solution Approach 1:
The patent segments the ECG signal into distinct morphological components (P-wave, QRS complex, ST-segment, T-wave) and analyzes each segment separately using distribution functions. This segmentation allows for precise measurement of specific cardiac events while maintaining the efficiency of non-invasive ECG observation, thereby resolving the contradiction between time efficiency and diagnostic accuracy.
Solution Approach 2:
The patent transforms the ECG signal from time-domain representation to distribution functions in the frequency domain, changing the parameter space for analysis. By using distribution functions instead of traditional ESD analysis, the method overcomes harmonic disintegration issues and improves measurement precision while maintaining the non-invasive and time-efficient characteristics of ECG observation.
2Ease of manufacture
If traditional morphological analysis of ECG is performed, then the method is simple and non-invasive, but the accuracy in detecting myocardial ischemia is poor with only 30 to 60% detection rate
Solution Approach 1:
The patent replaces traditional visual inspection methods with automated computational analysis using distribution functions. This substitution maintains the simplicity and non-invasive nature of ECG analysis while dramatically improving detection accuracy by using mathematical transformations to extract subtle ischemic patterns that are invisible to human observers.
Solution Approach 2:
The patent changes the analysis parameters from simple morphological measurements to distribution functions that capture frequency-domain characteristics. This parameter transformation enables the detection of ischemia with high accuracy while keeping the methodology straightforward and based on standard ECG recordings.
3Loss of information
If ESD analysis is used to analyze ECG harmonics, then frequency-domain information is obtained, but the results are difficult to interpret and correlate to morphology consistently
Solution Approach 1:
The patent segments the frequency-domain analysis into specific distribution functions corresponding to different morphological features (P-wave distribution, QRS distribution, ST-segment distribution, T-wave distribution). This segmentation makes the frequency-domain information interpretable by directly linking it to recognizable ECG morphological components, resolving the contradiction between information retention and ease of interpretation.
Data Source
AI summary
Disclosed herein methods, devices, and systems for detecting and diagnosing a heart disease or disorder in a subject from a prime electrocardiogram which comprises calculating at least one distribution function of the prime electrocardiogram and determining whether the distribution function is indicative of the presence of absence of the heart disease or disorder.