Exercise ECG High-Frequency QRS Analysis for Ischemia Detection
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Solution Overview
Problem
Existing methods for identifying heart health status based on ST-T segment data from electrocardiograms (ECGs) are inadequate as they fail to detect many potential heart problems, leading to reduced accuracy in assessing heart health.
Innovation Solution
An exercise electrocardiogram data analysis method that involves analyzing the high-frequency component of the QRS complex to determine a high-frequency QRS waveform curve, selecting reference points, and calculating the area of a waveform descent region to assess the attention level, which includes determining absolute and relative descent areas, and using reference indices to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ST-T segment data is used for heart health assessment, then the method is simple and widely applicable, but the accuracy of identifying heart problems is reduced
Solution Approach 1:
The patent segments the QRS complex into multiple components (Q wave, R wave, S wave) and further divides each wave into sub-segments (e.g., QR segment, RS segment, ST segment). This segmentation allows for more precise localization and analysis of pathological changes, improving diagnostic accuracy by examining specific portions of the waveform rather than treating the QRS complex as a single unit.
Solution Approach 2:
The patent introduces multiple new dimensions for ECG analysis beyond the traditional ST-T segment. It analyzes waveform descent areas, amplitude ratios, and temporal characteristics across different waves and segments. This multi-dimensional approach captures more information about myocardial ischemia and other heart conditions, significantly improving detection accuracy.
2Reliability
If traditional ECG analysis methods are used, then the analysis process is quick, but potential heart problems are missed leading to false negatives
Solution Approach 1:
The patent performs preliminary segmentation and identification of key waveform components (Q wave, R wave, S wave and their sub-segments) before conducting the actual pathological analysis. Reference lines and descent regions are pre-calculated and marked, which streamlines the subsequent diagnostic process and reduces analysis time despite the increased complexity of the analysis.
Solution Approach 2:
The system automatically identifies waveform characteristics, calculates descent areas, and generates diagnostic assessments without requiring manual measurement by clinicians. The automated calculation of multiple parameters (waveform descent areas, amplitude ratios, temporal characteristics) reduces both analysis time and human subjectivity, improving reliability.
Data Source
AI summary
Provided is an exercise electrocardiogram data analysis method, which includes: acquiring and analyzing exercise electrocardiogram data to obtain a high-frequency QRS waveform curve; selecting a first reference point and a second reference point from the high-frequency QRS waveform curve; according to the first reference point, the second reference point and the high-frequency QRS waveform curve, determining the area of a waveform descent area; and according to the area of the waveform descent area, determining the attention level.