Cardiography System Automating Hemodynamic Parameter Recognition
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Solution Overview
Problem
Current impedance cardiography systems lack the ability to accurately and automatically differentiate between diastolic and systolic congestive heart failure and other clinical conditions, relying heavily on human interpretation and lacking integration of signals from heart valve activity, which can lead to inaccurate diagnosis and inappropriate therapies.
Innovation Solution
A cardiography system and method that integrates automated recognition of hemodynamic parameters and waveform attributes using sensors, a knowledge base, and processing device to correlate cardiovasculogram signals with disease states, providing accurate identification of heart valve activity and suggesting appropriate goal-directed therapies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated computerized interpretation of waveforms is used, then diagnostic accuracy and consistency are improved, but the system complexity and requirement for comprehensive waveform analysis increase
Solution Approach 1:
The waveform analysis is divided into distinct segments (e.g., E-wave, a-wave, c-wave, d-wave, x-descent, y-descent) with specific identification criteria. Each segment is analyzed independently using dedicated algorithms, then integrated to provide comprehensive diagnostic output. This segmentation enables complex analysis to be managed through modular, manageable components.
Solution Approach 2:
The system uses an intermediary database of reference waveforms and hemodynamic parameters that mediates between raw waveform data and diagnostic conclusions. This knowledge base stores characteristic patterns and parameters, allowing the system to compare measured waveforms against established references to automatically identify disease states without requiring complex real-time computation.
2Reliability
If integration of multiple signal sources (ICG, phonocardiography, ECG) is implemented, then diagnostic reliability is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system merges multiple signal sources (ICG waveform, phonocardiographic signals, ECG) into a unified analysis framework. Waveform attributes from different sources are combined and correlated with hemodynamic parameters to provide comprehensive diagnostic assessment. The integration is achieved through a common processing architecture that handles multiple input types systematically.
Solution Approach 2:
The system employs a universal processing platform that can accommodate and analyze multiple types of signals (ICG, PCG, ECG) and hemodynamic parameters through a single integrated software architecture. This multi-functional design allows the same system to diagnose various cardiac conditions using different signal combinations without requiring separate specialized systems for each signal type.
3Measurement precision
If comprehensive waveform attribute analysis is performed, then differentiation between diastolic and systolic heart failure is improved, but the measurement time and processing requirements increase
Solution Approach 1:
The system performs preliminary identification and classification of waveform segments and attributes before integrating them into the final diagnostic assessment. Reference waveforms and hemodynamic parameter ranges are pre-established in the knowledge base, allowing rapid comparison against stored data rather than performing complex calculations in real-time during patient assessment.
Data Source
AI summary
A cardiography system and method using automated recognition of hemodynamic parameters and waveform attributes is provided. The cardiography system and method includes at least one sensor, a knowledge base and a processing device. The at least one sensor provides a waveform signal and a hemodynamic parameter input. The knowledge base includes data corresponding to various disease states. The processing device receives the waveform signal and hemodynamic parameter input from the sensor, identifies waveform attributes on the waveform signal, measures the waveform attributes, accesses the knowledge base, cross-references the waveform attributes and the hemodynamic parameters with data in the knowledge base, and outputs a suggested likelihood of a particular disease state. The knowledge base optionally includes goal-directed therapies associated with particular disease states for providing suggested goal-directed therapies based on the cross-referencing of the waveform attributes and the hemodynamic parameters with the knowledge base.