Neurological Detection Algorithm Training for Implantable Devices
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Solution Overview
Problem
Current seizure detection technologies for epilepsy patients are not reliable and energy-efficient, limiting therapeutic options for those resistant to antiepileptic drugs, as they require innovative approaches like closed-loop systems for interrupting seizure spread using electrical stimulation.
Innovation Solution
A computer program trains a neurological condition detection algorithm for implantable neurostimulation devices by selecting optimal electrode channels based on EEG data, adapting to the target electrode arrangement, and using pseudo-Laplacian patterns to reduce noise, allowing for efficient seizure detection and stimulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high-density EEG electrode systems are used for seizure detection, then detection reliability is improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent segments the EEG signal analysis by selecting and processing signals from specific electrode channels (such as F8, T4, T6, O2) rather than analyzing all available channels. This segmentation allows the system to achieve reliable seizure detection using a subset of electrodes, thereby reducing device complexity while maintaining detection effectiveness.
Solution Approach 2:
The patent applies local quality by focusing computational resources and analysis on specific electrode locations that are most relevant for seizure detection. By identifying and prioritizing signals from particular electrodes (e.g., temporal and occipital regions), the system achieves high detection reliability without requiring complex processing of all electrode channels.
2Measurement precision
If more electrode channels are used for training the detection algorithm, then detection accuracy is improved, but processing time and energy consumption increase
Solution Approach 1:
The patent extracts and utilizes only the most informative electrode channels for algorithm training and operation. By selecting specific channels (such as F8, T4, T6, O2) that provide the most discriminative information for seizure detection, the system achieves high detection accuracy while significantly reducing the computational burden and processing time compared to using all available channels.
Solution Approach 2:
The patent applies partial action by using a selective subset of electrode channels rather than the complete set. This approach provides sufficient detection accuracy for clinical purposes without the excessive computational requirements of processing all available channels, thus optimizing the balance between accuracy and processing efficiency.
3Reliability
If the detection algorithm is highly adapted to individual patient patterns, then detection reliability is improved, but programming complexity and time increase
Solution Approach 1:
The patent implements preliminary action by providing pre-configured electrode channel selections and detection parameters that can be applied across multiple patients. The system includes pre-defined electrode mappings (such as F8-T4, T6-O2 pairs) and detection algorithms that are prepared in advance, reducing the programming complexity required for individual patient setup while maintaining high detection reliability through subsequent customization if needed.
Data Source
AI summary
The invention relates to a computer program for training a neurological condition detection algorithm to be used for neurological condition detection in an implantable neurostimulation device having a target electrode arrangement, the computer program comprising the following steps: a) inputting EEG data in a computer which executes the computer program, the EEG data being recorded by at least one EEG from at least one patient using an electrode system with a plurality of electrode channels, b) identifying neurological activity in the EEG data, which corresponds to a neurological condition, based upon neurological condition identification tags included in the EEG data and/or input in the computer, c) selecting a subset of electrode channels out of the available electrode channels in the EEG data depending c1) on the identified neurological activity and/or c2) on characteristic data of the target electrode arrangement, d) training a neurological condition detection algorithm by using the EEG data only of the selected subset of electrode channels.