Myocardial Instability Detection via Reversal Point Analysis
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Solution Overview
Problem
Current implantable medical devices face challenges in accurately predicting the onset of sudden cardiac death due to limitations in monitoring myocardial instability, despite obtaining cardiac signals and other physiological parameters, as existing hardware restricts the use of comprehensive algorithms for predicting ventricular tachyarrhythmias.
Innovation Solution
A method and system that analyze myocardial instability by identifying reversal points in physiological parameters over cardiac cycles, calculating differences, and determining myocardial instability based on these points, with a microcontroller-directed approach to initiate responsive actions when instability is detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive algorithms are used to predict ventricular tachyarrhythmias, then prediction accuracy improves, but device hardware complexity increases beyond current implantable device capabilities
Solution Approach 1:
The patent segments the complex prediction algorithm into multiple simpler components: (1) detecting physiological parameters (ECG, impedance, pressure), (2) identifying reversal points in parameter trends, (3) calculating reversal point frequency, and (4) comparing against thresholds. This segmentation allows the algorithm to achieve high prediction accuracy while remaining implementable in current implantable device hardware.
Solution Approach 2:
The patent applies partial action by focusing on specific critical features (reversal points in physiological parameters) rather than analyzing all possible ECG waves and segments. This selective approach maintains prediction accuracy while reducing computational complexity to levels suitable for implantable devices.
2Device complexity
If current implantable devices are used, then device simplicity is maintained, but accurate prediction of sudden cardiac death is limited
Solution Approach 1:
The patent extracts and isolates the most diagnostically valuable feature - reversal points in physiological parameters - from the complex ECG signal. By focusing computational resources on detecting these specific reversal points rather than analyzing the entire signal spectrum, the system achieves accurate predictions within the hardware constraints of current implantable devices.
Solution Approach 2:
The patent transforms the prediction problem from analyzing complex waveforms to monitoring a simplified parameter - the frequency of reversal points. This parameter transformation maintains prediction accuracy while adapting the algorithm to work within the computational limitations of existing implantable device hardware.
Data Source
AI summary
A method of analyzing myocardial instability includes obtaining a physiological parameter representative of myocardial behavior over a set of cardiac cycles and determining reversal points in the physiological parameter over the set of cardiac cycles. The method also includes identifying myocardial instability based on the reversal points in the physiological parameter. A reversal point may correspond to a value of the physiological parameter, during a current cardiac cycle, that exceeds or is less than the values of the physiological parameter during prior and subsequent cardiac cycles. Optionally, the method includes calculating differences between values of the physiological parameter for consecutive cardiac cycles and detecting the reversal points when a current difference exceeds or is less than differences for prior and subsequent cardiac cycles.