Seizure Detection via Work Level Excursion Analysis
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Solution Overview
Problem
Current seizure detection methods are inadequate in accurately distinguishing between convulsive and non-convulsive seizures, particularly in varying activity levels and environmental conditions, leading to potential misclassification and inadequate response.
Innovation Solution
A medical device that determines a patient's work level by analyzing body signals such as arterio-venous oxygen differences, kinetic activity, and autonomic signals, using a work level excursion module to detect pathological excursions beyond predefined thresholds, which are dynamically adjusted based on factors like time of day, hydration status, and patient characteristics, to differentiate between seizure types and trigger appropriate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional seizure detection methods are used, then detection simplicity is maintained, but detection accuracy deteriorates due to inability to distinguish between convulsive and non-convulsive seizures
Solution Approach 1:
The patent segments the seizure detection process into multiple independent analysis components: work level determination from body signals, activity level determination from motion sensors, environmental condition monitoring, and threshold comparison. Each component processes specific data independently, then integrates results to classify seizure types, thereby improving detection accuracy without proportionally increasing overall system complexity
Solution Approach 2:
The patent adds a new dimension of analysis by introducing work level (derived from arterio-venous oxygen differences and metabolic signals) as an independent parameter alongside traditional motion-based activity level. This additional physiological dimension enables differentiation between convulsive and non-convulsive seizures that motion alone cannot distinguish
2Adaptability or versatility
If fixed thresholds are used for seizure detection, then device simplicity is maintained, but adaptability deteriorates across varying environmental conditions and patient states
Solution Approach 1:
The patent implements dynamic threshold adjustment where detection thresholds are continuously adapted based on real-time environmental conditions (temperature, humidity, altitude) and patient-specific factors (hydration status, body composition, activity level). The system automatically recalibrates thresholds to account for physiological variations during different states such as exercise, sleep, or fever, maintaining high adaptability while keeping the adjustment mechanism integrated and automated
Solution Approach 2:
The system incorporates feedback loops where detected seizures and false positives are used to refine future threshold settings. The device learns from accumulated data about individual patient patterns and environmental correlations, automatically adjusting thresholds to improve accuracy over time without requiring manual reconfiguration
3Measurement precision
If multiple body signals are analyzed to determine work level, then detection accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent merges multiple body signal sources (arterio-venous oxygen differences from optical sensors, kinetic activity from accelerometers, autonomic signals from galvanic skin response sensors) into a unified work level metric. By integrating these diverse signals through a combined analysis algorithm, the system achieves comprehensive physiological assessment while consolidating processing into a single coherent measurement that simplifies downstream interpretation
Data Source
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AI summary
We report a method of determining an occurrence of an epileptic convulsive seizure in a patient, comprising: receiving body data from a patient during a first time period, determining a work level relating to said first time period at least based partially upon said body data; determining whether said work level exceeds an extreme work level threshold; performing a responsive action, in response to a determination that said work level exceeds said extreme work level threshold. 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.