EEG Brain Connectivity Analysis for Cognitive Impairment Diagnosis
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Solution Overview
Problem
Current medical technologies for diagnosing degenerative brain diseases, such as dementia, are limited in accurately identifying specific diseases and making early diagnoses, often failing to differentiate between types and stages of cognitive impairments.
Innovation Solution
A cognitive impairment diagnosis method that utilizes electroencephalogram signals to analyze brain connectivity, extract features, and generate diagnosis information using a machine learning-based model to determine the severity and cause of cognitive impairments, enabling early detection and monitoring of disease progression and treatment responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional medical technologies are used to diagnose cognitive impairments, then the diagnostic process is simple, but the measurement precision and ability to differentiate specific diseases is insufficient
Solution Approach 1:
The patent segments the diagnostic process into multiple independent modules: EEG signal acquisition module, preprocessing module (including artifact removal and feature extraction), machine learning analysis module, and diagnosis report generation module. Each module performs a specific function, allowing the complex diagnostic task to be divided into manageable components while maintaining high measurement precision through specialized processing at each stage.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between raw EEG signals and diagnostic conclusions. These models serve as mediators that transform complex neural signals into interpretable diagnostic information, bridging the gap between raw data and clinical decision-making while improving diagnosis accuracy without requiring direct complex interpretation of raw signals.
2Measurement precision
If detailed analysis of EEG signals is performed to improve diagnosis accuracy, then the measurement precision improves, but the processing time and complexity increase
Solution Approach 1:
The patent performs preliminary preprocessing of EEG signals including artifact removal, filtering, and feature extraction before main analysis. By preparing the data in advance and removing irrelevant information early in the process, the system reduces the computational burden during critical analysis phases, thereby improving diagnosis accuracy while minimizing processing time loss.
Solution Approach 2:
The patent extracts only the most relevant features from EEG signals for diagnosis, removing redundant and irrelevant information. This selective extraction of critical features (such as specific frequency bands, connectivity metrics, and temporal patterns) maintains high diagnostic accuracy while significantly reducing the amount of data that requires intensive processing, thus decreasing overall processing time.
3Loss of information
If comprehensive diagnosis information is generated, then the information completeness improves, but the complexity of analyzing and interpreting the data increases
Solution Approach 1:
The patent employs a universal machine learning framework that can handle multiple diagnostic tasks simultaneously. The same core analysis system generates comprehensive diagnosis information including disease type classification, severity assessment, progression prediction, and treatment response evaluation, thereby maintaining information completeness while avoiding the need for separate complex processing systems for each diagnostic aspect.
Solution Approach 2:
The patent merges multiple analysis functions into a unified diagnostic system. By combining feature extraction, classification, regression, and prediction functions into an integrated machine learning pipeline, the system generates comprehensive diagnosis information while reducing the overall complexity compared to using separate independent systems for each analytical task.
Data Source
AI summary
Provided is a cognitive impairment diagnosis method including: receiving an electroencephalogram signal of a user by a cognitive impairment diagnosis device; preprocessing the electroencephalogram signal by the cognitive impairment diagnosis device; extracting features, by the cognitive impairment diagnosis device, from the electroencephalogram signal by using a brain connectivity-based analysis method; and outputting cognitive impairment diagnosis information of the user by the cognitive impairment diagnosis device, on the basis of features having a causal relationship with the cognitive impairment diagnosis information, among the extracted features.