Physiological Signal Analysis for Post-Procedural Cardiac Dysfunction
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Solution Overview
Problem
Structural heart interventions can lead to interruption or degradation of normal heart functionality due to electrical or mechanical pressures, necessitating a method to monitor and predict potential cardiac dysfunction post-procedure.
Innovation Solution
A system comprising sensors, a physiological signal processing system, and a feature analysis system to monitor and analyze electrocardiogram signals, extracting features indicative of cardiac function and predicting the risk of post-procedural dysfunction by comparing trend lines with a cohort patient population.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If structural heart intervention is performed to treat valvular heart disease and anatomic cardiac defects, then therapeutic benefit is achieved, but interruption or degradation of normal heart functionality occurs due to electrical or mechanical pressures
Solution Approach 1:
The system performs preliminary monitoring and risk prediction before actual cardiac dysfunction occurs by analyzing physiological signals and extracting features indicative of conduction abnormalities. This allows early detection of trends that precede overt dysfunction, enabling preventive intervention before the structural heart procedure causes irreversible damage.
Solution Approach 2:
The system continuously monitors physiological signals post-procedure and provides feedback through risk prediction algorithms. By comparing individual patient trend lines with cohort-based reference ranges, the system adjusts monitoring intensity and alerts clinicians when dysfunction is predicted, creating a closed-loop feedback system that responds to actual patient status.
2Measurement precision
If physiological signals are monitored and analyzed to detect cardiac dysfunction, then early detection capability is improved, but system complexity increases due to multiple sensors and processing components
Solution Approach 1:
The system uses a multi-functional integrated approach where sensor data acquisition, signal processing, feature extraction, and risk prediction are combined in a unified platform. The same sensor array serves multiple purposes: monitoring electrical activity, mechanical function, and hemodynamic parameters simultaneously, reducing the need for separate specialized devices.
Solution Approach 2:
The system introduces an intermediary processing layer that translates complex physiological signals into simplified risk predictions. Feature extraction algorithms act as intermediaries between raw sensor data and clinical decision-making, converting complex waveforms and patterns into interpretable risk scores and trend indicators.
3Measurement precision
If feature extraction and trend analysis are performed on physiological signals, then prediction accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by focusing feature extraction on the most clinically relevant parameters rather than analyzing all possible signal characteristics. By selecting key features indicative of conduction abnormalities and prioritizing them for analysis, the system achieves sufficient prediction accuracy without the computational burden of exhaustive feature sets.
Solution Approach 2:
The system performs preliminary feature extraction and trend calculation continuously in the background, so that when clinical decisions are needed, the analysis is already complete or near-complete. Pre-computing trend lines and comparing them against cohort reference ranges beforehand reduces real-time processing requirements during critical decision moments.
Data Source
AI summary
Systems and methods are provided for monitoring and processing physiological signals (e.g., electrocardiogram signals) to detect and predict for possible dysfunction of an anatomical feature (e.g., cardiac dysfunction) or otherwise predict a likelihood of future cardiac dysfunction of the individual. For example, a system comprises a plurality of sensors, a physiological signal processing system, and a feature analysis system. The sensors are configured to monitor physiological signals from an individual that has undergone a medical procedure on an anatomical feature. The physiological signal processing system is configured to analyze the physiological signals and extract features from the physiological signals which are indicative of a function of the anatomical feature. The feature analysis system is configured to analyze the extracted features and predict a risk of the individual developing a post-procedural dysfunction of the anatomical feature as a result of the medical procedure on the anatomical feature.