Cardiac Signal Regularity Analysis for Arrhythmia Detection
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Solution Overview
Problem
Current systems for myocardial ischemia and infarction analysis, particularly using ECG signals, are subjective, time-consuming, and lack reliability and accuracy, especially in emergency cases, and fail to provide comprehensive quantitative characterization of myocardial status and severity, particularly for intra-cardiac electrograms.
Innovation Solution
A system analyzes cardiac electrophysiological signals using ventricular depolarization signal regularity by calculating joint mutual distribution density and bilateral mutual regularity between ventricular and atrial depolarization responses within the same heart beat, enabling real-time detection and characterization of cardiac arrhythmias and heart tissue functions without relying on baseline signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If waveform morphology and time domain parameter analysis is used for cardiac arrhythmia monitoring, then cardiac events can be identified, but the analysis becomes subjective and time-consuming requiring extensive expertise
Solution Approach 1:
The patent transforms the analysis from subjective waveform morphology assessment to objective quantitative parameter measurement. It introduces new parameters including signal entropy, fractal dimension, and mutual information metrics that automatically characterize arrhythmia patterns without requiring expert interpretation, thereby reducing analysis time while maintaining or improving accuracy
Solution Approach 2:
The patent replaces the manual/expert-based mechanical interpretation process with an automated computational system. By using algorithms to calculate entropy, fractal dimensions, and statistical parameters, the system substitutes human expert analysis with machine-based objective measurement, eliminating subjectivity and reducing time requirements
2Reliability
If ST segment voltage deviation is used for myocardial ischemia detection, then ischemia events can be detected, but the method fails to indicate myocardial ischemia severity and lacks reliability
Solution Approach 1:
The patent moves beyond the single-dimensional ST segment voltage measurement by introducing multiple dimensional parameters including signal entropy, fractal dimension, and temporal variability metrics. This multi-dimensional analysis provides both detection capability and severity characterization, capturing the complexity of ischemic events that single-parameter analysis misses
Solution Approach 2:
The patent segments the ECG signal into multiple characteristic components (P wave, QRS complex, ST segment, T wave) and analyzes each with multiple parameters. This segmentation allows simultaneous detection of ischemia events and quantification of severity through combined analysis of various signal portions, providing comprehensive information that single-parameter methods cannot deliver
3Productivity
If single parameter analysis such as ST segment magnitude is used for ischemia event detection, then detection can be performed, but false alarms occur due to lack of comprehensive analysis
Solution Approach 1:
The patent merges multiple independent parameters (entropy, fractal dimension, statistical moments, temporal variability) into a comprehensive analysis framework. By combining these parameters that capture different aspects of signal behavior, the system achieves both rapid detection and high accuracy, reducing false alarms while maintaining fast response time
Solution Approach 2:
The patent creates a multi-functional analysis system that simultaneously performs detection, characterization, and severity assessment using a unified set of parameters. The same parameter set serves multiple purposes: detecting ischemia events, differentiating event types, and quantifying severity, thereby improving reliability without sacrificing detection speed
Data Source
AI summary
A system for heart performance characterization and abnormality detection comprises an input processor and a signal processor. The input processor receives first sampled data representing a first signal portion of a heart activity related signal and second sampled data representing a second signal portion of a heart activity related signal. The signal processor determines distribution data associated with degree of similarity between the first and second signal portions by determining a difference between (a) values derived by applying a first function to mean adjusted sampled values of the first signal portion and (b) values derived by applying a second function to mean adjusted sampled values of the second signal portion. In response to the determined distribution data, the signal processor initiates generation of a message associated with the degree of similarity between the first and second signal portions.