QRS Waveform Quantification Using High-Frequency ECG Analysis
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Solution Overview
Problem
Current ECG systems face challenges in automatically distinguishing between ischemic and healthy subjects during stress tests due to the difficulty in processing high-frequency ECG signals, which are sensitive to electrode contact and body motion, and lack real-time analysis capabilities for detecting ischemic events.
Innovation Solution
The development of an apparatus and method for quantifying QRS waveforms using high-frequency ECG signals, involving an input unit for receiving high-frequency ECG signals, a primary analyzer for calculating a primary index, and a secondary analyzer for deriving a secondary index, which provides on-line quantification of ischemic events or conditions, including the use of statistical functions and envelope analysis to indicate the presence and severity of ischemia.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-frequency ECG signals are used for detecting ischemic events, then measurement precision is improved, but device complexity increases due to sensitivity to electrode contact and body motion
Solution Approach 1:
The QRS complex is segmented into three distinct portions (initial, middle, and terminal) for separate analysis. Each portion is processed independently to extract specific features, allowing the system to manage the complexity of high-frequency signal analysis by breaking it down into manageable segments with distinct diagnostic values.
Solution Approach 2:
The patent transforms the time-domain QRS complex into the frequency domain by analyzing high-frequency components (150-250 Hz). This dimensional transformation from temporal to spectral analysis enables the detection of ischemic events that are not visible in traditional low-frequency ECG analysis, thereby improving measurement precision.
2Device complexity
If traditional ECG analysis methods are used, then device complexity is kept simple, but measurement precision deteriorates due to inability to detect high-frequency ischemic indicators
Solution Approach 1:
The system performs preliminary filtering and segmentation of the QRS complex before detailed analysis. By pre-processing the signal to isolate the QRS portion and divide it into meaningful segments, the system prepares the data in advance for more accurate high-frequency analysis, enabling better detection precision without requiring overly complex real-time processing.
Solution Approach 2:
The patent introduces an intermediary processing stage that bridges traditional ECG analysis and advanced high-frequency analysis. The system first identifies and isolates the QRS complex, then applies specific high-frequency filtering and segmentation as intermediate steps before final ischemic event detection. This intermediary approach maintains system simplicity while enabling precision detection.
3Productivity
If real-time analysis of high-frequency QRS complexes is implemented, then productivity is improved for ischemic event detection, but device complexity increases due to multiple analysis stages
Solution Approach 1:
The system applies periodic analysis to each detected QRS complex, processing them sequentially as they occur in real-time. By using a standardized periodic workflow (filtering → segmentation → feature extraction → classification) for each heartbeat, the system achieves real-time productivity while managing complexity through repetition of proven processing steps rather than requiring complex simultaneous multi-channel analysis.
Data Source
AI summary
An apparatus for QRS waveform quantifying, comprising: an input unit, for receiving one or more high frequency (HF) range QRS complexes from one or more ECG leads, a primary analyzer, for calculating a primary index from the high frequency (HF) range QRS complex, and a secondary analyzer, connected after the primary analyzer, for deriving a secondary index from the primary index, thereby to provide a quantification of QRS complexes.