Actiniform Segmentation for Cardiac Signal Analysis
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Solution Overview
Problem
Current clinical methods for detecting and characterizing cardiac arrhythmias, such as ventricular tachycardia and myocardial ischemia, are subjective, time-consuming, and lack objectivity, often resulting in false negatives and inadequate detection of early-stage cardiac abnormalities, particularly in noisy environments.
Innovation Solution
The method involves actiniform segmentation of cardiac electrophysiological signals, which categorizes waveforms into distinct portions based on a radiated distribution pattern centered at key time points, allowing for the extraction of actiniform parameters and ratios that monitor changes in the signals, enabling earlier detection of cardiac abnormalities and more accurate characterization of cardiac function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional waveform morphology and time domain parameter analysis are used for cardiac arrhythmia detection, then the method is simple and easy to implement, but the detection accuracy is insufficient and false negatives occur
Solution Approach 1:
The patent segments the cardiac waveform into distinct morphological components (P wave, QRS complex, ST segment, T wave) and further divides each into sub-components (e.g., Q wave, R wave, S wave). This segmentation allows detailed analysis of specific waveform portions to detect subtle changes that indicate arrhythmia, thereby improving detection accuracy without requiring complete waveform analysis
Solution Approach 2:
The patent introduces geometric morphology analysis by calculating angles between waveform segments (e.g., angle between QRS complex and ST segment) and area measurements. This transforms one-dimensional amplitude-time data into two-dimensional geometric features, enabling detection of subtle morphological changes that traditional time-domain analysis misses
2Loss of information
If sophisticated mathematical theories such as frequency analysis and nonlinear entropy evaluation are applied, then qualitative cardiac arrhythmia characterization is improved, but the ability to provide quantitative information on electrophysiological function and arrhythmia localization is lost
Solution Approach 1:
The patent segments the waveform into distinct morphological components and calculates specific geometric parameters (angles, areas) for each segment. This segmentation preserves quantitative information about electrophysiological function while enabling qualitative characterization through the geometric relationships between segments
Solution Approach 2:
The patent changes the analysis parameters from traditional time-domain measures to geometric morphology parameters (angles between waveforms, areas enclosed by waveforms). These parameter changes enable simultaneous quantitative measurement of electrophysiological function and qualitative assessment of arrhythmia characteristics
3Reliability
If ST segment voltage deviation analysis is used as the golden standard for myocardial ischemia detection, then the method is clinically established, but it cannot be used for intra-cardiac electrograms and cannot quantitatively characterize ischemia severity
Solution Approach 1:
The patent creates a universal geometric morphology analysis framework that can be applied to various waveform types (surface ECG, intra-cardiac electrograms) and various cardiac conditions (arrhythmia detection, ischemia characterization). The same angle and area calculation methods work across different applications, making the system versatile while maintaining clinical reliability
Solution Approach 2:
The patent changes the measurement parameter from voltage deviation (mV) to geometric morphology parameters (angles, areas). This parameter transformation enables the method to work with intra-cardiac electrograms where voltage thresholds are not applicable, while also enabling quantitative characterization of ischemia severity through geometric changes
4Ease of operation
If clinical expertise and experience are required for accurate interpretation, then the analysis can be performed with simple tools, but the method becomes subjective and time-consuming
Solution Approach 1:
The patent replaces the mechanical system of human expert interpretation with an automated computational system. The geometric morphology parameters are calculated automatically through algorithms that measure angles and areas, eliminating subjectivity while maintaining operational simplicity. The system objectively quantifies waveform characteristics without requiring clinical expertise
Data Source
AI summary
Disclosed herein is a framework for facilitating patient signal analysis. In accordance with one aspect, actiniform segmentation is performed on patient signal data waveform based on an actiniform shape. The actiniform shape is centered at a peak of the waveform and includes connection lines extending from the peak to key time points of the patient signal data waveform. Actiniform parameters may be extracted from the segmented patient signal data waveform. Additionally, one or more actiniform ratios may be determined based on the actiniform parameters to monitor changes in the patient signal data waveform.