Automated Cardiac Characteristic Calculation System
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Solution Overview
Problem
Conventional methods for calculating the Systolic Area Index (SAI) require manual selection of right ventricle to left ventricle pressure curves, which is inaccurate and requires extensive clinical experience, leading to potential misdiagnosis in differentiating constrictive pericarditis and restrictive myocardial disease.
Innovation Solution
A system that acquires hemodynamic and cardiac impedance signals, processes them to automatically identify corresponding portions of the signals, and calculates cardiac characteristics, including SAI, using a flow diagram process involving signal filtering, peak determination, and adaptive multi-cycle detection to provide accurate and efficient calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of right ventricle to left ventricle pressure curves is used, then clinical expertise can be applied to identify corresponding curves, but the process requires extensive clinical experience and is prone to inaccuracy
Solution Approach 1:
The system automatically identifies corresponding right ventricle and left ventricle pressure curves using signal processing algorithms that analyze temporal relationships and morphological features. The processor autonomously selects curves based on predefined criteria without requiring manual intervention, thereby eliminating the need for extensive clinical experience while maintaining or improving accuracy.
Solution Approach 2:
The manual mechanical process of curve selection by clinicians is replaced with an automated electronic signal processing system. The processor uses algorithmic methods to detect and select corresponding pressure curves based on temporal synchronization and signal characteristics, substituting human expertise with computational analysis.
2Productivity
If manual curve selection is used, then flexibility in handling complex cases is maintained, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary automated identification and selection of corresponding pressure curves before the actual SAI calculation. By pre-processing the signal data to identify and tag corresponding curves using temporal and morphological analysis, the system prepares the data structure in advance, enabling rapid subsequent calculations without time-consuming manual intervention.
Solution Approach 2:
The automated system independently performs the entire curve selection and identification process without requiring clinician time or effort. The processor autonomously analyzes the pressure signals, identifies corresponding right and left ventricle curves, and prepares them for calculation, thereby dramatically improving productivity while minimizing time loss.
3Measurement precision
If automated signal processing is implemented, then calculation accuracy is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary processing layer between raw signal acquisition and final SAI calculation. This intermediary layer includes the processor that performs automated curve identification, temporal correspondence analysis, and signal matching. While this adds computational complexity, it enables high precision by systematically analyzing signal characteristics and temporal relationships that would be difficult to assess manually.
Solution Approach 2:
The signal processing is divided into distinct functional segments: signal acquisition, temporal correspondence analysis, curve identification, and SAI calculation. Each segment performs a specific function with dedicated algorithms, making the overall complex process manageable and verifiable. This segmentation allows the system to achieve high precision through systematic analysis while organizing complexity into modular, testable components.
Data Source
AI summary
A system and method includes reception of a hemodynamic signal, reception of a cardiac impedance signal, identification of a first peak and a second peak of the cardiac impedance signal, identification of a first portion of the hemodynamic signal based on the first peak and a second portion of the hemodynamic signal based on the second peak, and calculation of a cardiac characteristic based on the first portion and the second portion of the hemodynamic signal.