Brain Signal Assessment Using Real-Time Neural Data Clustering

The patent segments the data processing workflow into two distinct parts: (1) real-time neural data analysis and clustering performed by the implantable medical device to identify patient states, and (2) batch processing of clustered data performed by external devices. This segmentation allows the implantable device to focus only on essential real-time operations, reducing its complexity while maintaining high measurement precision through immediate patient state identification.

The patent implements preliminary action by performing neural data analysis and patient state identification in real-time within the implantable device before data is transmitted externally. This preliminary processing ensures that patient states are identified immediately when they occur, improving measurement precision and response time without requiring complex real-time processing capabilities in external devices.

The patent divides the processing functions between the implantable medical device and external devices. The implantable device performs only neural data analysis, clustering, and patient state identification in real-time, while external devices handle less time-critical tasks such as long-term data storage and comprehensive analysis. This segmentation reduces the real-time processing burden on the implantable device, limiting complexity increase while achieving faster therapy adjustments.

The patent introduces data clustering as an intermediary step between raw neural data collection and patient state identification. The processing circuitry first clusters neural data based on similarity, then identifies patient states from these clusters. This intermediary approach simplifies the real-time processing requirements by working with condensed data representations rather than raw high-dimensional neural data, reducing device complexity while maintaining fast response times.

The patent implements self-service by enabling the implantable medical device to automatically perform neural data analysis, clustering, and patient state identification without requiring continuous user input. The system autonomously monitors neural data, identifies patient states in real-time, and can trigger alerts or therapy adjustments automatically. This self-service capability maintains high reliability of patient state information while significantly reducing the ease of operation burden on users.

The patent incorporates feedback mechanisms where the system presents identified patient states to users for confirmation and uses this feedback to improve future identifications. The feedback loop allows the system to learn from user corrections and refine its patient state detection algorithms over time, enhancing reliability while requiring minimal user interaction compared to manual reporting systems.