High Frequency QRS Analysis for Ischemia Detection
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Solution Overview
Problem
Current ECG analysis methods for detecting myocardial ischemia, particularly through high frequency components of the QRS complex, face challenges in accurately identifying relative reductions in amplitude during stress conditions, which are indicative of ischemia or infarction, often resulting in false negatives or requiring multiple tests.
Innovation Solution
The proposed solution involves an apparatus and method that quantify features in the QRST waveform by analyzing high frequency components above 100 Hz, using a combination of primary and secondary HF analyzers to derive indices from ECG signals, and applying a decision algorithm to detect relative and absolute reductions in amplitude, thereby diagnosing ischemia or infarction with improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ECG analysis methods are used to detect myocardial ischemia, then the detection process is simple, but the accuracy is low resulting in false negatives
Solution Approach 1:
The patent segments the QRS complex analysis into multiple frequency components, specifically isolating high frequency components (HFC) above 100 Hz from the conventional ECG signal. This segmentation allows independent analysis of different frequency bands, enabling detection of ischemia through HFC amplitude changes without being confounded by lower frequency components, thereby improving detection accuracy while maintaining manageable system complexity through modular signal processing
Solution Approach 2:
The patent introduces a new dimension of analysis by examining the amplitude of high frequency components as a separate diagnostic parameter beyond conventional ECG metrics. This dimensional expansion adds the HFC amplitude measurement (a new parameter space) to the traditional ECG analysis framework, enabling detection of ischemia through changes in this additional dimension without requiring complete replacement of existing analysis methods
2Measurement precision
If high frequency component analysis is implemented, then detection accuracy improves, but the device complexity increases
Solution Approach 1:
The patent applies preliminary signal processing actions including band-pass filtering to isolate high frequency components before main analysis, and averaging of multiple cardiac cycles to enhance signal quality. These preliminary actions prepare the signal in advance, reducing noise and artifacts before the actual ischemia detection algorithm is applied, thereby improving measurement precision while keeping the core detection logic relatively simple
Solution Approach 2:
The patent replaces complex mechanical or invasive diagnostic procedures with electronic signal processing methods. Specifically, it substitutes physical stress testing or invasive monitoring with non-invasive ECG-based high frequency component analysis, using computational methods to extract diagnostic information from routine ECG signals, thus improving accuracy without proportionally increasing physical device complexity
3Reliability
If relative amplitude reduction detection is used, then false negatives are reduced, but the requirement for stress condition monitoring increases
Solution Approach 1:
The patent enables continuous monitoring of high frequency component amplitude during stress conditions, allowing real-time detection of ischemia without requiring intermittent or repeated testing. The continuous acquisition and analysis of HFC amplitude provides an uninterrupted diagnostic stream, improving reliability by capturing transient ischemic events while maintaining testing efficiency through automated real-time analysis rather than requiring multiple discrete test sessions
4Measurement precision
If multiple ECG parameters are analyzed, then diagnostic accuracy improves, but the time required for analysis increases
Solution Approach 1:
The patent extracts and focuses specifically on the high frequency component amplitude parameter from the full ECG signal spectrum, isolating this single most diagnostic parameter for ischemia detection. By extracting only the relevant HFC amplitude information rather than analyzing all ECG parameters equally, the system maintains high diagnostic accuracy while reducing analysis time, as the focused extraction allows rapid calculation without processing unnecessary parameters
Data Source
AI summary
Detecting cardiac ischemia by detecting local changes in high frequency ECG parameters. Local changes may be, for example, local reduction in RMS of high frequency components, for example, during a stress test.