ECG Analysis Using Multi-Domain Signal Segmentation
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Solution Overview
Problem
Current automated electrocardiography (ECG) analysis systems face challenges in accurately diagnosing heart conditions due to the morphological nature of ECG waveforms, loss of information from different heart muscle parts, and high false positive rates, limiting their clinical adoption despite the heart being a nonlinear system with complex variations.
Innovation Solution
The development of systems that utilize signal processing to detect subwaveforms within the P, Q, R, S, T, and J waveforms, and multi-domain ECG analysis using different frequency bands to provide more detailed information about heart muscle anatomy, combined with artificial intelligence for pattern recognition and annotation of cardiac electrophysiological signals as normal or abnormal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ECG waveform analysis is used, then the system is simple to operate, but the diagnostic accuracy is low due to morphological nature and loss of information
Solution Approach 1:
The patent segments the ECG analysis process into multiple domains: time domain waveform analysis, frequency domain spectral analysis, and harmonic component analysis. Each domain extracts specific features that, when combined, provide comprehensive diagnostic information while maintaining manageable system complexity through modular processing.
Solution Approach 2:
The patent transforms the conventional single-dimensional time-domain ECG waveform into multi-dimensional analysis by adding frequency domain and harmonic domain perspectives. This dimensional expansion reveals hidden patterns and provides more diagnostic parameters without overwhelming system complexity through structured computational approaches.
2Loss of information
If multi-domain ECG analysis is implemented, then detailed anatomical information is provided, but the device complexity increases
Solution Approach 1:
The patent divides the complex multi-domain analysis into separable processing stages: orthogonal waveform generation, spectral density calculation, harmonic component extraction, and pattern recognition. Each stage processes specific aspects of the ECG signal independently, preserving comprehensive information while avoiding the need for a monolithic complex system.
3Productivity
If automated pattern recognition is used, then productivity increases, but false positive rates remain high
Solution Approach 1:
The patent implements feedback mechanisms where pattern recognition results are continuously refined by comparing against multiple reference patterns across different domains (time, frequency, harmonic). The system adjusts its classification thresholds and weighting based on the consistency of patterns across domains, reducing false positives while maintaining high diagnostic throughput through automated multi-criteria evaluation.
Data Source
AI summary
An ECG system identifies and annotates cardiac electrophysiological signals in an ECG waveform from harmonic waveforms. 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. At least one subwaveform or one or more discontinuity points are identified as a normal or abnormal electrophysiological signal of the ECG waveform from the comparison. The ECG waveform is displayed along with one or more markers at a location of the at least one subwaveform or one or more discontinuity points.