Dynamic Heart Rate Threshold for Seizure Detection
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Solution Overview
Problem
Current cardiac-based seizure detection algorithms for epilepsy patients suffer from high rates of false positive and false negative detections due to their inability to account for the patient's physical activity level, leading to inaccurate seizure event identification.
Innovation Solution
A medical device that dynamically adjusts heart rate-based seizure detection thresholds based on the patient's activity level, applying higher thresholds during sedentary activities to prevent false positives and lower thresholds during strenuous activities to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed heart rate thresholds are used for seizure detection, then the detection algorithm is simple to implement, but false positive and false negative rates are high
Solution Approach 1:
The patent implements dynamic threshold adjustment based on the patient's activity level. The system transitions from fixed thresholds to activity-dependent thresholds, where the threshold value changes according to the detected activity state (e.g., resting vs. exercising). This resolves the contradiction by making the detection algorithm adaptive rather than static, improving reliability without excessive complexity increase.
Solution Approach 2:
The patent changes the threshold parameter dynamically based on activity level detection. Instead of using a single fixed threshold, the system adjusts the heart rate threshold parameter according to the patient's physiological state, thereby improving detection accuracy while maintaining computational feasibility through predefined activity-based threshold sets.
2Measurement precision
If higher detection sensitivity is applied, then more seizures are detected, but false positive detections increase
Solution Approach 1:
The patent applies different detection thresholds locally based on the patient's activity state. Instead of using a uniform high sensitivity threshold across all conditions, the system implements activity-specific thresholds that are locally optimized for each physiological state, thereby reducing false positives while maintaining high sensitivity when appropriate.
Solution Approach 2:
The system dynamically adjusts detection sensitivity based on real-time activity level detection. During high-activity periods, the threshold is raised to reduce false positives, while during low-activity periods, the threshold is lowered to maximize seizure detection sensitivity, thus resolving the contradiction between sensitivity and false positive rate.
3Reliability
If activity level consideration is added to the detection algorithm, then detection accuracy improves, but device complexity increases
Solution Approach 1:
The patent segments the detection process into distinct activity-level-based modes. The system divides the operational space into activity states (e.g., resting, moderate activity, vigorous activity), each with its own predefined threshold parameters. This segmentation approach improves accuracy by accounting for activity variations while controlling complexity through modular, state-based logic.
Solution Approach 2:
The system manages complexity by implementing parameter changes based on predefined activity categories rather than continuous complex analysis. The detection algorithm switches between predetermined threshold sets corresponding to different activity levels, improving accuracy through activity awareness while maintaining computational efficiency through discrete parameter switching.
Data Source
AI summary
A method may include sensing a time of beat sequence of a patient's heart and processing said time of beat sequence with a medical device to identify a change in heart rate of a patient from a first heart rate to a second heart rate. The method may continue by determining with the medical device at least one of a) a ratio of the second heart rate to the first heart rate and b) a difference between the second heart rate and the first heart rate. The method may include determining with the medical device at least one of a) a dynamic ratio threshold for the ratio and b) a dynamic difference threshold for the difference, wherein the at least one threshold is based upon the first heart rate. The ratio and/or the difference may be compared to the threshold(s) to detect a neurological event, for example, an epileptic seizure.