Epileptic Seizure Detection via Dynamic Work Level Thresholds
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Solution Overview
Problem
Current medical technologies face challenges in accurately detecting epileptic seizures based on body signals, as they fail to effectively correlate changes in body signals with work levels, leading to false positives and negatives.
Innovation Solution
A method and system for detecting epileptic seizures by obtaining a time series of body signals, determining a current body signal value, and comparing it with a reference value while considering the patient's work level, to identify an ictal component and issue a seizure detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If body signal thresholds are used for seizure detection without considering work level, then detection simplicity is maintained, but measurement precision deteriorates due to false positives and negatives
Solution Approach 1:
The patent implements dynamic threshold adjustment based on patient work level. The system continuously monitors work level signals and dynamically modifies the body signal thresholds accordingly, transforming static thresholds into adaptive, context-aware thresholds that reduce false detections while maintaining system simplicity
Solution Approach 2:
The system changes the detection parameters (thresholds) based on the work level parameter. By adjusting the threshold values according to the patient's current work level state, the system achieves more accurate seizure detection without requiring complex additional hardware
2Measurement precision
If multiple body signals are correlated with work level for detection, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent combines multiple body signal types (e.g., heart rate, respiratory rate, temperature) with work level signals into a unified detection framework. By merging these signals and evaluating them collectively against adjusted thresholds, the system achieves improved detection accuracy while managing complexity through integrated processing
3Reliability
If work level correlation is implemented for ictal component identification, then reliability of seizure detection improves, but processing time increases
Solution Approach 1:
The system performs preliminary work by continuously monitoring and pre-processing work level signals in real-time. This preliminary action allows the system to have work level data ready when body signals are analyzed, enabling faster correlation and reducing the time penalty associated with reliability-improving correlation analysis
Data Source
AI summary
We report a method of a method for detecting an epileptic seizure, comprising providing a first body signal reference value; determining a current body signal value from a time series of body signals; comparing the current body signal value and the first reference value; determining a work level of the patient; determining whether the current body signal value comprises an ictal component, based on the work level and the comparing; issuing a detection of an epileptic seizure in response to the determination that the current body signal value comprises the ictal component; and taking at least one further action (e.g. warning, delivering a therapy, etc.), based on the detection. We also report a medical device system configured to implement the method. We also report a non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform the method.