Seizure Detection Algorithm Adjustment via Patient Activity
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Solution Overview
Problem
Current seizure detection algorithms in medical systems for managing neurological disorders like epilepsy often face challenges in accurately distinguishing between true seizures and false positives or false negatives, leading to inappropriate therapy delivery.
Innovation Solution
A medical system that utilizes a combination of bioelectrical brain signals and patient activity parameters, such as motion or posture, to adjust the seizure detection algorithm, ensuring accurate identification of target seizures and minimizing false detections by correlating bioelectrical brain signal characteristics with patient activity information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a seizure detection algorithm is made more sensitive to detect all possible seizures, then the detection capability improves, but false positive alerts increase
Solution Approach 1:
The patent combines multiple patient parameters (bioelectrical brain signals, patient activity, and additional physiological parameters) into a unified detection system. By merging these different data sources, the system achieves more accurate seizure detection while reducing false positives, as the combination of parameters provides a more comprehensive view of seizure activity compared to any single parameter alone.
Solution Approach 2:
The system dynamically adjusts detection parameters and thresholds based on the correlation between different patient parameters. By changing the weightings and thresholds of detection parameters based on observed patterns in the data, the system optimizes its sensitivity to detect true seizures while minimizing false positive alerts.
2Measurement precision
If the seizure detection algorithm uses multiple patient parameters to improve accuracy, then detection precision improves, but device complexity increases
Solution Approach 1:
The patent segments the detection system into distinct modules that process different patient parameters independently (bioelectrical signals module, activity monitoring module, physiological parameter module). Each module processes its specific parameter type and contributes to the overall detection decision, making the complex multi-parameter system more manageable and easier to implement.
Solution Approach 2:
The system uses a universal processing framework that can handle multiple types of patient parameters through a common algorithmic structure. This multi-functional approach allows the same detection architecture to process bioelectrical signals, activity data, and physiological parameters, reducing the need for separate dedicated processing systems for each parameter type.
3Reliability
If therapy delivery is triggered on every detected seizure event, then seizure management effectiveness improves, but inappropriate therapy delivery increases
Solution Approach 1:
The system incorporates feedback mechanisms that continuously monitor the correlation between detected seizure events and actual patient outcomes. By analyzing whether therapy delivery followed by parameter changes indicates successful seizure management, the system adjusts its detection thresholds and triggering mechanisms to ensure therapy is delivered only when truly necessary, reducing inappropriate deliveries while maintaining management effectiveness.
Data Source
AI summary
A medical system implements a seizure detection algorithm to detect a seizure based on a first patient parameter. The medical system monitors a second patient parameter to adjust the seizure detection algorithm. In some examples, the medical system determines whether a seizure for which therapy delivery is desirable occurred based on a second patient parameter. If a target seizure occurred, and the seizure detection algorithm did not detect the target seizure, the medical system adjusts the seizure detection algorithm to detect the target seizure. For example, the medical system may determine a first patient parameter characteristic indicative of the target seizure detected based on the second patient parameter and store the first patient parameter characteristic as part of the seizure detection algorithm. In some examples, the first patient parameter is an electrical brain signal and the second patient parameter is patient activity (e.g., patient motion or posture).