Multi-Stream Epilepsy Detection System Using Metric Classification
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Solution Overview
Problem
Current diagnostic tools inadequately detect and manage extreme epileptic events such as Convulsive Status Epilepticus (CSE) and Sudden Unexpected Death in Epilepsy (SUDEP), primarily focusing on brain activity while neglecting cardio-respiratory and metabolic changes that contribute to morbidity and mortality.
Innovation Solution
A method that involves receiving body data streams to determine autonomic, neurological, metabolic, and endocrine indices, detecting seizure events, and performing classification analyses to identify seizure metrics, allowing for the classification of seizures into classes and the detection of extreme events, with responsive actions including therapy adjustments and warnings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If current diagnostic tools focus only on brain activity, then seizure detection is simplified, but detection accuracy of extreme epileptic events deteriorates
Solution Approach 1:
The patent combines multiple data streams from different physiological systems (neurological EEG data, cardiovascular ECG/heart rate, respiratory breathing patterns, metabolic glucose levels, and endocrine hormone levels) into a unified monitoring system. This multi-system integration allows the diagnostic tool to detect extreme epileptic events like CSE and SUDEP with higher accuracy by capturing the systemic physiological changes that occur during these events, rather than relying solely on brain activity monitoring.
2Measurement precision
If multi-system monitoring is implemented, then detection accuracy of extreme events improves, but device complexity increases
Solution Approach 1:
The monitoring system is designed with multi-functionality to handle diverse physiological data streams. A single integrated platform collects, processes, and analyzes neurological, cardiovascular, respiratory, metabolic, and endocrine data using common hardware and software infrastructure. The system performs multiple functions including real-time seizure detection, extreme event identification, pattern recognition across systems, and automated alert generation, thereby managing complexity through universal design rather than separate specialized systems.
Solution Approach 2:
The patent introduces an intermediary processing layer that receives raw data from multiple physiological sensors and transforms it into meaningful clinical indicators. This intermediary layer includes algorithms that integrate data across systems, identify correlated changes, and generate composite metrics for extreme event detection. By placing this intermediary processing layer between the diverse sensors and the decision-making system, the patent manages complexity through standardized data transformation and integration protocols.
3Manufacturing precision
If classification analysis is performed on all seizure events, then seizure management precision improves, but processing time increases
Solution Approach 1:
The classification system applies different levels of analysis to different seizure events based on their characteristics and clinical significance. Routine seizures receive standard classification processing, while events showing patterns consistent with extreme epileptic events (such as prolonged duration, abnormal physiological parameter changes, or clustered occurrence) trigger enhanced multi-system classification analysis. This localized quality approach ensures high precision for critical events while maintaining efficient processing for routine cases.
Solution Approach 2:
The system performs preliminary screening of seizure events using automated algorithms that quickly identify characteristics suggesting extreme events. Before full classification analysis is initiated, the system pre-processes data to detect early warning signs and prioritizes events requiring detailed classification. This preliminary action allows the system to prepare classification resources in advance and focus comprehensive analysis only on events where it is most needed, reducing overall processing time while maintaining precision.
Data Source
AI summary
A method for identifying changes in an epilepsy patient's disease state, comprising: receiving at least one body data stream; determining at least one body index from the at least one body data stream; detecting a plurality of seizure events from the at least one body index; determining at least one seizure metric value for each seizure event; performing a first classification analysis of the plurality of seizure events from the at least one seizure metric value; detecting at least one additional seizure event from the at least one determined index; determining at least one seizure metric value for each additional seizure event, performing a second classification analysis of the plurality of seizure events and the at least one additional seizure event based upon the at least one seizure metric value; comparing the results of the first classification analysis and the second classification analysis; and performing a further action.