EEG Cognitive Reserve Assessment for Early Decline Detection
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Solution Overview
Problem
Existing methods for determining cognitive reserve are subjective and prone to errors, relying on proxies like educational attainment and lifestyle surveys, which can lead to inaccurate assessments and unnecessary aggressive treatments.
Innovation Solution
A system utilizing EEG-based metrics, such as slow-wave activity (SWA), to objectively assess cognitive reserve by training a model on biometric data, providing more accurate predictions and enabling real-time monitoring of cognitive decline.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subjective proxy measures (education, lifestyle surveys) are used to assess cognitive reserve, then the assessment process is simple and accessible, but the measurement precision and reliability are poor leading to inaccurate assessments
Solution Approach 1:
The patent replaces subjective proxy measures with objective biometric measurements from EEG sensors. Instead of relying on self-reported education and lifestyle data, the system uses physiological signals (slow-wave activity, sleep spindles, K-complexes) directly measured from the brain to objectively assess cognitive reserve, thereby improving measurement precision while maintaining ease of operation through automated data collection
Solution Approach 2:
The patent introduces EEG signals as an intermediary between the brain's cognitive function and the assessment system. The EEG data serves as a direct window into brain activity patterns that reflect cognitive reserve, providing a more accurate mediator for assessment than subjective proxies while enabling automated, objective measurement
2Measurement precision
If EEG-based objective measures are used to assess cognitive reserve, then measurement precision and reliability improve, but device complexity and data processing requirements increase
Solution Approach 1:
The patent extracts specific, pre-identified EEG features (slow-wave activity, sleep spindles, K-complexes) from the complex EEG signal. By focusing on these specific temporal patterns rather than processing the entire EEG signal, the system reduces computational complexity while maintaining high measurement precision for cognitive reserve assessment
Solution Approach 2:
The patent segments the EEG signal into distinct temporal patterns and sleep stages (N1, N2, N3, REM). This segmentation allows the system to process and analyze specific cognitive-relevant features separately, reducing the computational burden while improving the accuracy of cognitive reserve measurement through targeted analysis of sleep-stage-specific patterns
3Reliability
If early detection of cognitive decline is implemented, then intervention effectiveness improves, but the difficulty of detecting and measuring early signs increases
Solution Approach 1:
The patent performs preliminary assessment of cognitive reserve using EEG-based measures before significant cognitive decline occurs. By establishing a baseline of sleep-stage-specific patterns (slow-wave activity, spindles, K-complexes) in cognitively healthy individuals, the system enables early detection of deviations that signal emerging cognitive decline, allowing intervention before severe impairment occurs
Solution Approach 2:
The patent implements continuous monitoring of EEG patterns during sleep to provide feedback on cognitive reserve status. By repeatedly measuring sleep-stage-specific features over time, the system can detect subtle changes in brain activity patterns that indicate cognitive decline, enabling early intervention while maintaining high reliability through objective, repeatable measurements
Data Source
AI summary
In some examples, a system includes a device comprising one or more electroencephalogram (EEG) sensors. The device is configured to collect, from a subject, an EEG signal using the one or more EEG sensors. The system further includes one or more processors; and a memory storing instructions that, when executed by the processors, cause the one or more processors to receive, from the one or more EEG sensors of the device, the EEG signal collected from the subject; and apply a model to the EEG signal to determine a cognitive reserve of the subject. The model is trained using a plurality of sets of training data, each set of training data including a training EEG dataset collected from a training subject and a training cognitive reserve score corresponding to the training subject that is determined based on metrics separate from the training EEG dataset.