EEG Analysis System Hierarchical Cognitive Impairment Classification
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Solution Overview
Problem
Current dementia diagnosis platforms based on EEG data analysis can only dichotomously classify patients as normal or demential, failing to identify individuals who may develop dementia in the future, and lack effective early screening methods.
Innovation Solution
A method and system for analyzing EEG data to classify users into severe and non-severe cognitive impairment groups, further subdividing them into normal, amnestic mild cognitive impairment, within normal limits, preclinical Alzheimer's disease, non-AD mild cognitive impairment, and prodromal AD groups, using quantitative EEG data generation and optimized classification models to assess memory impairment and dementia risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional EEG-based dementia diagnosis platforms dichotomously classify patients as normal or demential, then the classification process is simple and fast, but the diagnostic precision is insufficient to identify early-stage or at-risk individuals
Solution Approach 1:
The patent segments the single dichotomous classification into multiple hierarchical classification stages. First, users are classified into severe cognitive impairment group or non-severe cognitive impairment group. Then, the non-severe group is further subdivided into normal group or amnestic mild cognitive impairment group. Finally, the normal group is classified into within normal limits group or preclinical Alzheimer's disease group. This multi-level segmentation enables precise identification of early-stage and at-risk individuals while maintaining systematic organization.
Solution Approach 2:
The patent transitions from a one-dimensional binary classification (normal/demential) to a multi-dimensional classification system with multiple hierarchical levels and subgroups. This dimensional expansion allows the system to capture the spectrum of cognitive impairment from normal aging to severe dementia, enabling both early detection and comprehensive diagnostic precision.
2Measurement precision
If comprehensive multi-level classification is implemented to screen early-stage dementia risk, then the diagnostic precision is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent implements preliminary action by first classifying users into broad categories (severe vs. non-severe cognitive impairment) before proceeding to more detailed sub-classifications. This preliminary segmentation allows the system to quickly identify high-risk groups that require further investigation while potentially expediting the process for lower-risk individuals, thereby reducing overall processing time while maintaining comprehensive diagnostic precision.
Solution Approach 2:
The classification process is designed dynamically, where the depth of classification can be adjusted based on initial results and clinical needs. The system can stop at different hierarchical levels depending on the required diagnostic precision and available resources, making the processing time flexible while maintaining the capability for comprehensive analysis when needed.
3Loss of information
If detailed classification into multiple subgroups is performed, then the information completeness is improved, but the system complexity increases
Solution Approach 1:
The patent segments the classification system into distinct hierarchical modules, where each level handles specific aspects of cognitive impairment assessment. This modular segmentation organizes the complex information processing into manageable segments, reducing system complexity while maintaining complete information capture across all classification levels.
Solution Approach 2:
The patent resolves information completeness versus system complexity by organizing detailed classification information along hierarchical dimensions rather than requiring all details simultaneously. The multi-level structure allows comprehensive information capture (severe/non-severe distinction, normal/aMCI distinction, WNL/preclinical AD distinction) while presenting results in an organized, manageable format that reduces perceived system complexity.
Data Source
AI summary
Provided is a method for classifying severe cognitive impairment patients by analyzing electroencephalogram (EEG) data. The method of classifying severe cognitive impairment patients by analyzing EEG data includes a brainwave collection step of collecting EEG data on a plurality of users, a first classification step of classifying the plurality of users into a severe cognitive impairment group or a non-severe cognitive impairment group by analyzing the collected EEG data, a second classification step of classifying users included in the non-severe cognitive impairment group into a normal group or an amnestic mild cognitive impairment (aMCI) group, and a third classification group of classifying users included in the normal group into a within normal limits (WNL) group or a preclinical Alzheimer's disease (AD) group and classifying users included in the aMCI group into a non-AD MCI group or a prodromal AD group.