Schizophrenia Diagnosis Using EEG Brain Wave Classification
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Solution Overview
Problem
Current diagnostic methods for schizophrenia, such as fMRI, face limitations including high costs, spatial and temporal constraints, and low reliability, which can cause anxiety in patients and do not accurately classify the disorder based on pathological criteria.
Innovation Solution
A method and device utilizing brain wave data and brain activity data to classify schizophrenia through machine learning models, specifically a first classification model to determine the occurrence of schizophrenia and a second classification model to classify symptoms, including positive, negative, and cognitive/disorganization symptoms, providing highly reliable information for diagnosis and prognosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fMRI is used for schizophrenia diagnosis, then diagnostic capability is provided, but cost increases and spatial-temporal limitations occur
Solution Approach 1:
The patent replaces the mechanical/imaging-based fMRI system with an electrical signal-based EEG system. Instead of using magnetic resonance imaging to capture brain activity, the invention uses electroencephalography to detect electrical brain waves, thereby reducing spatial and temporal constraints while lowering costs.
Solution Approach 2:
The patent employs cost-effective EEG equipment and algorithms compared to expensive fMRI machines. By using readily available EEG hardware and processing algorithms, the system achieves diagnostic capability at a fraction of the cost and complexity of fMRI infrastructure.
2Reliability
If fMRI is used for schizophrenia diagnosis, then diagnostic information is obtained, but patient anxiety increases
Solution Approach 1:
The patent uses a non-invasive EEG system that is more comfortable for patients compared to fMRI. The lightweight headgear and lack of confined space reduce patient anxiety while maintaining diagnostic accuracy through electrical signal analysis.
3Ease of operation
If superficial symptoms are used for schizophrenia diagnosis, then diagnosis process is simple, but diagnostic accuracy decreases
Solution Approach 1:
The patent replaces subjective clinical assessment with objective electrical signal measurement. By analyzing EEG patterns, the system provides quantitative, accurate diagnosis without relying on potentially inaccurate superficial symptom reports, thereby improving measurement precision while maintaining operational simplicity through automated analysis.
Solution Approach 2:
The patent introduces EEG signals as an intermediary between patient behavior and diagnostic conclusion. Instead of directly interpreting complex behavioral symptoms, the system uses brain wave patterns as a mediator to objectively determine diagnostic criteria, improving accuracy while keeping the process simple through automated signal processing.
Data Source
AI summary
The present invention provides a method for providing information about schizophrenia, implemented by a processor, the method comprising: receiving an individual's brain wave data; generating brain activity data based on the brain wave data; determining whether the individual's schizophrenia has occurred, using a first classification model configured to classify schizophrenia based on the brain activity data; and determining subtypes of schizohrenia using a second classification model, and a device using the method.