ECG I-point and J-point detection using frequency domain segmentation
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Solution Overview
Problem
Current automated electrocardiography (ECG) analysis systems face challenges in accurately measuring and annotating key parameters like the I-point and J-point due to waveform deformation, instability, and loss of standard points, leading to high false positives and limited clinical adoption.
Innovation Solution
The development of an automated ECG analysis system using signal processing to detect subwaveforms within the P, Q, R, S, T, and J waveforms, and multi-domain ECG techniques to separate frequency bands, enabling precise measurement and annotation of these parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ECG waveform measurement is used, then the system is simple and easy to operate, but the measurement precision of I-point and J-point is poor due to waveform deformation and loss of standard points
Solution Approach 1:
The patent segments the ECG waveform analysis into multiple frequency domains using Fourier transform. The signal is divided into different frequency components (0.5-15 Hz, 15-40 Hz, 40-100 Hz, 100-250 Hz, 250-500 Hz) and analyzed separately in each domain. This segmentation allows detection of characteristic points in specific frequency bands where they are most prominent, improving measurement precision while managing complexity through systematic frequency-domain decomposition
Solution Approach 2:
The patent transitions from time-domain analysis to frequency-domain analysis by applying Fourier transform. This dimensional change from time to frequency domain enables detection of I-point and J-point characteristics that are not visible or are distorted in the conventional time-domain waveform, thereby improving measurement accuracy without significantly increasing operational complexity
2Productivity
If automated ECG analysis is implemented, then productivity is improved, but the reliability is poor due to high false positives from waveform variations
Solution Approach 1:
The patent divides the ECG signal into multiple frequency bands and analyzes each band separately to detect specific waveform features. By segmenting the analysis across frequency domains, the system can identify characteristic points more reliably despite waveform variations, reducing false positives while maintaining automated processing efficiency
Solution Approach 2:
The patent uses frequency-domain representation as an intermediary between the raw time-domain ECG signal and the final diagnostic parameters. This intermediary transformation reveals hidden characteristics of I-point and J-point that are obscured in the time domain, enabling more reliable automated detection and reducing false positive results
3Measurement precision
If conventional single time domain waveform measurement is used, then the device complexity is low, but the measurement precision of ECG parameters is poor due to waveform deformation and non-linear variations
Solution Approach 1:
The patent applies Fourier transform to convert the single time-domain waveform into multiple frequency-domain representations. This dimensional change from time to frequency domain enables precise measurement of ECG parameters by revealing characteristics that are distorted or invisible in the time domain, improving accuracy while the systematic approach manages processing complexity
4Measurement precision
If multi-domain ECG techniques are used to separate frequency bands, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent segments the ECG signal into five distinct frequency bands (0.5-15 Hz, 15-40 Hz, 40-100 Hz, 100-250 Hz, 250-500 Hz) and analyzes each band separately. This segmentation improves measurement precision by identifying characteristic points in their optimal frequency domains, while the systematic segmentation approach provides a structured framework that manages system complexity
Data Source
AI summary
An ECG system measures and annotates the I-point of 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. A discontinuity point is identified as the I-point of the ECG waveform from the comparison. The ECG waveform is displayed along with a marker at a location of the discontinuity point.