AI ECG Analysis System for Quantitative ST-T Interval Detection
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Solution Overview
Problem
Current ECG analysis systems face challenges in accurately measuring and diagnosing myocardial infarction due to reliance on qualitative morphological changes in waveforms, lack of consistent mapping points, and high false positive rates, limiting their clinical adoption and accuracy.
Innovation Solution
The development of an automated electrocardiography (ECG) analysis system that uses signal processing to detect subwaveforms within the P, Q, R, S, T, and J waveforms, and applies artificial intelligence to characterize frequency domain signals, compare them to normal and abnormal databases, and provide quantitative indicators for the ST-T interval, enhancing diagnostic accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ECG analysis relies on qualitative morphological changes in waveforms, then the system is simple to operate, but the diagnostic accuracy and measurement precision are insufficient
Solution Approach 1:
The patent segments the ECG waveform into multiple distinct components (P wave, QRS complex, T wave, ST segment) and analyzes each segment separately using signal processing techniques. This segmentation enables precise measurement of morphological parameters in each segment, resolving the contradiction by transforming qualitative waveform analysis into quantitative segment-specific measurements while maintaining operational simplicity through automated processing.
Solution Approach 2:
The patent transforms the ECG analysis from qualitative morphological assessment to quantitative parameter measurement by extracting specific parameters (amplitude, duration, area) from different waveform segments. This parameter transformation enables precise diagnostic measurements while the automated calculation methods keep the system operationally simple, resolving the contradiction between measurement precision and device complexity.
2Reliability
If ECG analysis uses qualitative morphological patterns, then the device complexity is low, but the reliability and consistency of diagnosis are poor due to lack of standardized mapping points
Solution Approach 1:
The patent applies preliminary signal processing and filtering actions to the ECG waveform before analysis to standardize the input data. By pre-processing the signal to enhance specific components and remove noise, the system establishes consistent baseline conditions for measurement, improving diagnostic reliability while the automation of this process prevents excessive complexity increase.
Solution Approach 2:
The patent replaces manual visual assessment (mechanical/human system) with automated signal processing and computational analysis (electronic/computational system). This substitution provides standardized, repeatable measurements through algorithmic processing, significantly improving diagnostic consistency and reliability while the automated nature of the system manages complexity through software rather than hardware complexity.
3Measurement precision
If traditional ECG methods are used, then the ease of operation is high, but the false positive rate is high due to inability to detect subtle waveform changes
Solution Approach 1:
The patent adds quantitative dimensional measurements (amplitude, duration, area) to the traditional qualitative waveform analysis. By measuring parameters in multiple dimensions rather than relying solely on visual pattern recognition, the system detects subtle waveform changes that indicate pathology, improving detection accuracy while automated calculation maintains operational simplicity.
Solution Approach 2:
The patent creates quantitative copies (numerical representations) of the waveform characteristics through parameter extraction. Instead of directly interpreting complex waveforms, the system measures and records specific parameters (amplitude, duration, area) that replicate the essential diagnostic information in a standardized format, improving detection precision while simplifying the analysis process through automated parameter calculation.
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. An ST-T interval is identified from the plurality of subwaveforms and discontinuity points based on the comparison, the ST-T interval is divided into N number of equally spaced sections, and an average data value of detection is calculated for each section. A table is displayed that includes an average data value of detection for each section of the ST-T interval.