ECG Frequency Parameter Analysis for STEMI Diagnosis
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Solution Overview
Problem
Current methods for diagnosing ST-segment elevation myocardial infarction (STEMI) using ECG signals focus primarily on statistical parameters and spectra of RR intervals, neglecting the analysis of complete ECG signals, which limits their accuracy and effectiveness, especially in ultra-short-term recordings essential for wearable health applications.
Innovation Solution
A method and apparatus for analyzing ECG frequency parameters by obtaining signals from ST-elevated and reference leads, calculating power spectrum model-based parameters such as mean and median frequencies, and their shift ratios, to differentiate STEMI patients from healthy subjects, with alerts triggered for abnormal conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical parameters and spectra of RR intervals are used for STEMI diagnosis, then the diagnosis can be performed with simple equipment, but the measurement precision and diagnostic accuracy are insufficient
Solution Approach 1:
The patent segments the ECG signal analysis into multiple frequency bands (0.05-100 Hz divided into lower and higher frequency ranges) and calculates separate power spectral density parameters for each segment. This allows comprehensive analysis of ECG signals while maintaining computational efficiency by processing frequency components independently rather than analyzing the entire signal at once.
Solution Approach 2:
The patent transitions from time-domain analysis (RR interval statistics) to frequency-domain analysis (power spectral density, mean frequency, median frequency). This dimensional transformation enables extraction of additional diagnostic information from the ECG signals that was not accessible through traditional time-domain methods, thereby improving diagnostic accuracy without requiring more complex hardware.
2Measurement precision
If complete ECG signal analysis is performed, then diagnostic accuracy improves, but the analysis time and processing complexity increase
Solution Approach 1:
The patent performs preliminary filtering and segmentation of the ECG signal into frequency bands before conducting the full spectral analysis. By pre-processing the signal to isolate relevant frequency components (0.05-100 Hz range, with subdivisions at lower and higher frequencies), the subsequent analysis can focus computational resources on the most diagnostically relevant portions of the signal, reducing overall processing time.
Solution Approach 2:
The patent changes the analysis parameters by calculating multiple frequency-domain metrics (power spectral density at different frequency ranges, mean frequency, median frequency, frequency ratios) from the segmented ECG data. This parameter transformation allows comprehensive diagnostic evaluation to be achieved through efficient mathematical operations on frequency components rather than exhaustive time-domain processing.
3Adaptability or versatility
If ultra-short-term ECG recordings are used, then the application to wearable devices is enabled, but the reliability of diagnosis is reduced
Solution Approach 1:
The patent applies partial spectral analysis to ultra-short-term ECG recordings by focusing on specific frequency bands (lower frequency range 0.05-5 Hz and higher frequency range 5-100 Hz) rather than performing complete spectral analysis. This partial action approach extracts sufficient diagnostic information from the limited recording duration to achieve reliable STEMI detection while maintaining compatibility with wearable device constraints.
Solution Approach 2:
The patent combines multiple analysis approaches (time-domain RR interval measurement, frequency-domain power spectral density, mean frequency, median frequency, and frequency ratios) into a composite diagnostic system. This composite approach leverages the strengths of each method to achieve reliable diagnosis from ultra-short-term recordings by compensating for the limitations of individual methods when applied to brief signal segments.
4Measurement precision
If frequency domain parameters are calculated from ECG signals, then the diagnostic capability is enhanced, but the computational requirements increase
Solution Approach 1:
The patent segments the frequency spectrum into distinct ranges (lower frequency 0.05-5 Hz and higher frequency 5-100 Hz) and calculates power spectral density parameters independently for each segment. This segmentation allows the computational workload to be distributed across smaller, more manageable frequency intervals, reducing the energy required for each individual calculation while maintaining comprehensive diagnostic coverage.
Solution Approach 2:
The patent applies different analytical approaches to different frequency components based on their local diagnostic value. By identifying and prioritizing specific frequency bands (particularly the lower frequency range for HRV analysis and higher frequency range for waveform characteristics), the system concentrates computational resources on the most informative frequency regions, reducing overall energy consumption while maintaining high diagnostic capability.
Data Source
AI summary
This application provides a method and apparatus of analyzing the ECG frequency parameters with applications for the diagnosis of ST-segment elevation myocardial infarction (STEMI) diseases, which relates to the interdisciplinary field of biomedical and science engineering. The method includes obtaining ECG signals from subjects through the designed electrodes; calculating ECG frequency domain parameters of the subjects based on the proposed power spectrum model and getting the analytical validation results after studying and verifying the parameters; generating indicators based on the analytical validation results, which could be potentially used as alternative indicators for STEMI diagnosis; and alerting when the indicators meet preset abnormal conditions. The present embodiment is a powerful tool to diagnose STEMI diseases faster and more effectively and helps patients receive timely assistance and treatment.