Pareto Optimization of Seizure Detection and Therapy Parameters
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Solution Overview
Problem
The subjective nature of seizure definition complicates automated detection, leading to inconsistent validation and characterization, and existing methods rely solely on physiologic factors for seizure detection, lacking specificity and efficiency.
Innovation Solution
A method and system for automated detection and treatment of medical conditions using extra-brain signals, optimizing parameters through a Pareto-optimal approach to minimize the Cost of Event Intervention (CoEI) by iteratively adjusting detection and therapy parameters based on multiple metrics, including false positives, false negatives, and patient quality of life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If visual analysis by experts is used for seizure detection, then detection capability is achieved, but objectivity and reproducibility deteriorate due to subjective interpretation
Solution Approach 1:
The patent replaces the mechanical/visual analysis system with an automated computational algorithm that processes physiological signals. The system uses objective mathematical criteria and signal processing techniques to detect seizures, eliminating subjective human interpretation while maintaining detection capability through automated pattern recognition in EEG and other physiological data.
Solution Approach 2:
The system enables self-service by allowing the detection algorithm to automatically analyze physiological signals and make seizure detection decisions without requiring continuous expert visual analysis. The algorithm independently processes the data, applies detection criteria, and generates results, making the system autonomous and reproducible.
2Measurement precision
If automated detection algorithms are developed, then objectivity improves, but detection accuracy deteriorates due to lack of explicit seizure definition
Solution Approach 1:
The patent applies parameter changes by using multiple physiological parameters (heart rate, respiratory rate, temperature, activity levels) rather than relying on a single definition. The system dynamically adjusts detection thresholds and criteria based on individual patient baselines and contextual factors, allowing objective detection while adapting to capture the variability of seizure presentations.
Solution Approach 2:
The system achieves universality by developing a multi-functional detection approach that can identify various types of seizures and abnormal events through a unified algorithmic framework. The detection system handles multiple signal types and detection scenarios, making it broadly applicable while maintaining objectivity through consistent computational criteria.
3Reliability
If multiple physiological factors are considered for detection, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex detection task into separate modules, each handling specific physiological signals (cardiovascular, respiratory, thermal, activity). Each module processes its designated signal type independently using specialized algorithms, then results are integrated to form the overall detection decision. This modular approach improves accuracy through comprehensive analysis while managing complexity through structured organization.
Solution Approach 2:
The system merges multiple physiological signal streams and detection results into a unified detection framework. By combining information from heart rate, respiratory rate, temperature, and activity sensors, the system achieves improved detection accuracy through multi-parameter analysis while using integrated processing to manage the complexity of handling multiple data sources.
4Reliability
If detection thresholds are lowered to reduce false negatives, then sensitivity improves, but false positives increase
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors detection performance and adjusts thresholds based on observed false positive and false negative rates. The algorithm learns from actual patient data and detection outcomes, dynamically optimizing the balance between sensitivity and specificity. This feedback loop allows the system to maintain high sensitivity while minimizing false positives through adaptive threshold adjustment.
Solution Approach 2:
The system applies dynamics by making detection thresholds flexible and adaptive rather than fixed. Thresholds are dynamically adjusted based on individual patient baselines, time of day, activity state, and other contextual factors. This dynamic approach allows the system to maintain appropriate sensitivity across varying conditions while reducing false positives that would occur with static, overly sensitive thresholds.
Data Source
AI summary
A system and method for finding a Pareto-optimal solution for automated detection, warning, and abatement of a medical condition based on a cost of event intervention in a patient is disclosed. The method includes acquiring at least one biological signal from the patient via at least one sensor of a medical device, detecting an abnormal biological event based on changes in the biological signal, and delivering at least one of a therapy and a warning. The method includes logging a set of parameters including at least one of a detection parameter, a therapy parameter, and a therapy modality. The method includes finding an optimal set of parameters that yield a Pareto-optimal cost of event intervention by iteratively determining at least one metric over a time window, determining the cost of event intervention, and modifying at least one parameter, until the cost of event intervention meets an acceptability criteria.