Implantable Multisensor Monitoring for Predictive Event Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current diagnostic methods for neurological, vestibular, cochlear, and sleep disorders suffer from limitations such as limited recording windows, unreliable self-reported data, and challenges in accurately detecting and classifying episodic events, leading to sub-optimal treatment regimens with potential side effects.
Innovation Solution
Implantable sensor arrays coupled with processor devices and AI models to monitor and classify electrical and physiological signals, enabling long-term, reliable detection, prediction, and classification of events, and optimizing treatment protocols based on real-time data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic methods with limited recording windows are used, then device complexity is reduced, but measurement precision and reliability of event detection deteriorate
Solution Approach 1:
The patent segments the monitoring system into multiple independent components: sensor arrays for data collection, processor devices for signal processing, and AI models for event classification. This segmentation allows each component to be optimized independently, improving overall measurement precision while managing system complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by continuously collecting and pre-processing physiological data over extended periods before actual event detection is needed. This allows the AI models to be trained on comprehensive datasets and enables the system to quickly respond to events when they occur, improving detection accuracy without requiring complex real-time processing of all historical data.
2Reliability
If self-reported data collection is used, then ease of operation is improved, but reliability of diagnostic information deteriorates
Solution Approach 1:
The system employs self-service mechanisms where implantable sensors automatically collect physiological data without requiring patient intervention. The sensors continuously monitor electrical and physiological signals, and the AI models automatically classify events, eliminating the need for patients to manually report symptoms while ensuring high data reliability through objective, continuous measurement.
Solution Approach 2:
The patent replaces the mechanical system of manual patient reporting with an automated electronic sensing and processing system. Implantable sensors and AI algorithms substitute for patient self-reporting, providing more reliable diagnostic information while reducing patient burden through hands-free, continuous monitoring.
3Reliability
If treatment regimens are optimized based on comprehensive event detection, then therapeutic benefit is improved, but device complexity and treatment cost worsen
Solution Approach 1:
The system implements feedback loops where detected events and their classifications are used to continuously optimize treatment regimens. The AI models analyze detected events, determine therapeutic responses, and adjust treatment parameters accordingly. This feedback mechanism improves treatment effectiveness by enabling data-driven, personalized therapy optimization while managing complexity through automated decision-support algorithms.
Solution Approach 2:
The system optimizes treatment by dynamically changing therapeutic parameters based on detected events and patient responses. The AI models analyze physiological data to determine optimal dosing, timing, and type of treatment interventions, allowing personalized parameter adjustment that improves effectiveness while the modular system architecture manages the complexity of multiple adjustable parameters.
Data Source
AI summary
Methods and systems implement a variety of sensors, including in embodiments various combinations of EEG sensors, biochemical sensors, photoplethysmography (PPG) sensors, microphones, and accelerometers, to detect, predict, and/or classify various physiological events and/or conditions related to epilepsy, sleep apnea, and/or vestibular disorders. The events can include neuroelectrical events, cardiac events, and/or pulmonary events, among others. In some cases, the method and systems implement trained artificial intelligence (AI) models to detect, classify, and/or predict. The methods and systems are also capable of optimizing a treatment window, suggesting treatments that may improve the overall well-being of the patient (including improving pre-or post-event symptoms and effects), and/or interacting with various care providers.