Time-Frequency ECG Analysis for Myocardial Ischemia Detection
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Solution Overview
Problem
Conventional electrocardiogram (ECG) analysis struggles with ambiguities and inaccuracies in diagnosing heart conditions due to subtleties and complexities in electrical signals, particularly in identifying waveforms and features, which can lead to misdiagnosis.
Innovation Solution
The implementation of advanced time-frequency analysis through methods like short-time Fourier transform and wavelet transforms to convert ECG signals into two-dimensional time-frequency maps, enhancing the visualization and interpretation of cardiac activity by spreading out spectral components, allowing for improved diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ECG analysis methods are used, then the analysis process is simple and quick, but the diagnostic accuracy is low and ambiguities in identifying waveforms occur
Solution Approach 1:
The patent transforms the conventional one-dimensional time-domain ECG signal into a two-dimensional time-frequency map through time-frequency transformation. This dimensional expansion allows spectral components to be spread out and visualized, enabling more accurate identification of waveforms and detection of abnormalities such as myocardial ischemia that are not visible in traditional ECG analysis.
2Loss of information
If time-frequency transformation is applied to ECG signals, then the visualization of cardiac activity is improved and diagnostic accuracy increases, but the computational complexity and processing time increase
Solution Approach 1:
The patent divides the ECG signal processing into distinct stages: signal acquisition, time-frequency transformation, map generation, and analysis. This segmentation allows for optimized processing at each stage and enables the system to handle the computational complexity efficiently while maintaining the benefits of enhanced visualization and diagnostic accuracy.
Data Source
AI summary
Electrocardiograms can be analyzed in the time-frequency domain, following conversion into time-frequency maps, to determine characteristics or features of various waveforms, such as waveform morphology and/or the amplitude(s) and location(s) (in time and/or frequency) of one or more extrema of the waveform. Based on comparison of the extrema against thresholds and/or against each other, disease conditions may be determined.