ST Segment Subwaveform Detection in ECG Analysis
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Solution Overview
Problem
Conventional ECG systems face challenges in accurately measuring and analyzing the ST segment and other ECG parameters due to morphological changes, deformation, and instability, leading to inaccurate diagnosis and reliance on qualitative data, which limits their clinical application and accuracy.
Innovation Solution
The development of an automated electrocardiography (ECG) analysis system that uses signal processing to detect subwaveforms within the ST segment and other intervals, employing multi-domain ECG and artificial intelligence to provide quantitative data and improve diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ECG systems are used for ST segment analysis, then the system is simple and easy to operate, but the measurement precision and diagnostic accuracy deteriorate due to morphological changes and deformation
Solution Approach 1:
The patent segments the ST segment into multiple sub-intervals (ST1, ST2, ST3, ST4) based on waveform characteristics and inflection points. This segmentation allows precise measurement of each sub-interval separately, improving overall measurement accuracy while providing detailed morphological analysis that conventional systems cannot achieve
Solution Approach 2:
The patent introduces multi-domain analysis by transforming the ECG signal from time-domain only to include frequency-domain and time-frequency-domain representations. This dimensional expansion enables detection of subtle changes in ST segment morphology that are invisible in conventional time-domain analysis, significantly improving measurement precision
2Reliability
If conventional ECG analysis methods are used, then the system is simple, but the reliability deteriorates due to inability to detect subtle changes and high misdiagnosis rates
Solution Approach 1:
The patent implements automated detection algorithms that provide feedback on ST segment morphology, sub-interval measurements, and abnormality detection. The system continuously monitors ECG signals, compares measurements against established criteria, and provides real-time diagnostic feedback, significantly improving reliability by reducing human error and subjectivity
Solution Approach 2:
The patent replaces manual visual analysis of ECG waveforms with automated computer-based detection systems. The automated system uses algorithms to objectively measure ST segment parameters, detect subwaveforms, and identify abnormalities, eliminating the subjectivity and variability inherent in manual analysis and thereby improving diagnostic reliability
3Loss of information
If conventional ECG waveform analysis is used, then the device is simple, but the loss of information increases due to inability to capture local anatomical signal details
Solution Approach 1:
The patent segments the ST segment into four distinct sub-intervals (ST1-ST4) based on waveform inflection points and morphological characteristics. This segmentation preserves local anatomical information by analyzing each sub-interval separately, capturing subtle variations in ST segment morphology that would be lost in conventional holistic analysis
Solution Approach 2:
The patent applies multi-domain signal processing including frequency-domain transformation and time-frequency analysis to the ECG signal. This dimensional expansion reveals hidden local anatomical information and subtle morphological changes that are not visible in conventional time-domain analysis, significantly reducing information loss
4Measurement precision
If automated detection algorithms are implemented, then measurement precision improves, but the device complexity increases
Solution Approach 1:
The automated detection algorithm segments the ST segment into multiple sub-intervals and applies specific detection rules to each segment. This segmentation approach improves measurement precision by focusing analysis on specific morphological features while keeping the overall system structure manageable through modular processing
Solution Approach 2:
The patent implements automated detection for specific critical parameters (ST segment duration, sub-interval measurements, morphology) rather than attempting to automate all ECG analysis functions. This partial automation approach achieves significant precision improvement for the most clinically relevant measurements while limiting overall system complexity
Data Source
AI summary
Electrical impulses are received from a beating heart. The electrical impulses are converted to an ECG waveform. The ECG waveform is converted to a frequency domain waveform, which, in turn, is separated into two or more different frequency domain waveforms, which, in turn, are converted into a plurality of time domain cardiac electrophysiological subwaveforms and discontinuity points between these subwaveforms. The plurality of subwaveforms and discontinuity points are compared to a database of subwaveforms and discontinuity points for normal and abnormal patients or to a set of rules developed from the database. An ST segment and one or more ST subwaveforms within the ST segment are identified from the plurality of subwaveforms and discontinuity points based on the comparison. The ECG waveform with the one or more ST subwaveforms within the ST segment is displayed.