Cardiac Pathology Detection via Blood Pressure Waveform Complexity
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Solution Overview
Problem
Current clinical methods for blood pressure analysis fail to provide comprehensive and effective characterization of cardiac function, particularly in detecting early changes and deviations due to cardiac malfunctions, as they often rely on qualitative diagnosis and are prone to noise interference, lacking quantitative analysis of blood pressure waveforms.
Innovation Solution
A system that analyzes blood pressure waveform data using hemodynamic waveform signal coherence to identify and characterize cardiac disorders, employing complexity and spectrum coherence calculations to determine nonlinear changes and deviations, combining hemodynamic and electrophysiological signals for improved sensitivity and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If known clinical methods use blood pressure signals for qualitative diagnosis, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent transforms the analysis approach by changing from qualitative visual inspection to quantitative parameter extraction. It calculates specific hemodynamic parameters (stroke volume, cardiac output, systemic vascular resistance) and waveform characteristics (amplitude, duration, morphology) from blood pressure signals, converting subjective qualitative assessment into objective quantitative measurement with higher precision.
Solution Approach 2:
The patent replaces the manual qualitative analysis mechanism with an automated computational system. It uses signal processing algorithms and mathematical models to automatically extract hemodynamic parameters and generate diagnostic information, substituting the mechanical process of visual inspection with electronic computation to achieve both ease of operation and measurement precision.
2Measurement precision
If known systems use electrophysiological signals for cardiac disease detection, then sensitivity to electrical changes is improved, but reliability deteriorates due to noise interference
Solution Approach 1:
The patent combines multiple signal sources and analysis methods to improve reliability. It integrates analysis of blood pressure waveform morphology, hemodynamic parameter calculations, and temporal pattern recognition to compensate for limitations of any single method, creating a more robust diagnostic system that maintains sensitivity while reducing susceptibility to noise.
Solution Approach 2:
The patent uses hemodynamic parameter calculations as intermediary variables that translate raw blood pressure signals into clinically meaningful metrics. By computing stroke volume, cardiac output, and other derived parameters, it creates intermediate representations that are more reliable and less susceptible to noise than direct signal analysis, while preserving sensitivity to pathological changes.
3Productivity
If known methods focus on stroke volume and cardiac output calculation, then productivity is improved, but measurement precision deteriorates due to incomplete waveform analysis
Solution Approach 1:
The patent segments the blood pressure waveform into distinct phases and features for separate analysis. It identifies and measures specific waveform characteristics including amplitude, duration, morphology, and temporal relationships between different phases of the cardiac cycle. This segmented approach allows comprehensive extraction of diagnostic information while maintaining efficient automated processing.
Solution Approach 2:
The patent extends the analysis from traditional single-parameter stroke volume and cardiac output calculations to multi-dimensional waveform characterization. It analyzes amplitude dimension, temporal dimension, morphological features, and derived hemodynamic parameters simultaneously, adding multiple analytical dimensions to achieve comprehensive precision without sacrificing processing efficiency.
Data Source
AI summary
A system for heart performance characterization and abnormality detection includes an interface for receiving digitized electrical signals representing blood pressure waveforms over one or more heart beat cycles. The digitized electrical signals comprise, a first digital data sequence representing normal blood pressure of a patient, a second digital data sequence representing random blood pressure of a normal patient and a third digital data sequence representing a potentially abnormal blood pressure of a patient. A complexity processor calculates first, second and third complexity indices for the corresponding first, second and third digital data sequences respectively. A correlation processor uses the calculated first, second and third complexity indices to calculate one or more measures indicating deviation of the potentially abnormal blood pressure of the patient from a normal value.