EEG Signal Clustering for Automated Psychiatric Diagnosis
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Solution Overview
Problem
Existing analysis of EEG signals relies heavily on manual expert recognition and lacks automation, leading to inefficiencies and inaccuracies, with historical data not being effectively utilized for supplementation or normalization.
Innovation Solution
A computer-implemented method using advanced neural network analysis (ANNA) that captures EEG signals with multiple sensors, clusters them using objective and subjective data, and predicts medical plans or diagnoses through machine learning algorithms, enabling rapid psychiatric diagnosis and treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual expert recognition is used for EEG signal analysis, then diagnostic accuracy can be maintained through expert judgment, but analysis efficiency and productivity are significantly reduced
Solution Approach 1:
The patent replaces the manual mechanical process of expert visual inspection and interpretation of EEG signals with an automated computer-implemented system using machine learning algorithms and neural networks. The system processes raw EEG data through multiple analysis modules including artifact detection, feature extraction, and pattern recognition algorithms to automatically generate diagnostic recommendations, thereby eliminating the time-consuming manual review process while maintaining diagnostic accuracy through sophisticated computational analysis.
Solution Approach 2:
The system enables self-service by allowing the automated analysis pipeline to independently process EEG signals without requiring continuous expert intervention. The machine learning models are trained on historical data and can autonomously perform artifact rejection, signal classification, and diagnostic pattern recognition, freeing experts from routine analysis tasks while preserving their ability to review and validate complex cases when needed.
2Productivity
If statistical methods with simple thresholds are used for EEG analysis, then processing speed and productivity are improved, but measurement precision and diagnostic accuracy deteriorate
Solution Approach 1:
The patent transforms the analysis approach by changing from simple threshold-based statistical parameters to multi-dimensional feature parameters extracted through signal processing techniques. The system computes numerous features including frequency domain characteristics, time-domain statistics, and spatial patterns across multiple EEG channels, then uses machine learning algorithms to integrate these parameters for accurate classification. This parameter transformation enables both high processing speed through efficient computational algorithms and high diagnostic accuracy through comprehensive feature analysis.
Solution Approach 2:
The system employs a composite analytical approach by combining multiple analysis techniques including artifact detection algorithms, feature extraction methods, and classification models into an integrated diagnostic system. Rather than relying on a single threshold-based method, the patent synthesizes results from various analytical components to produce robust diagnostic outcomes, similar to how composite materials combine different properties to achieve superior performance.
3Device complexity
If historical EEG data is not normalized and integrated, then data storage and management remain simple, but the ability to supplement current analysis with historical patterns is lost
Solution Approach 1:
The system performs preliminary normalization and standardization of historical EEG data during the data ingestion phase, transforming raw historical records into a unified format that can be efficiently queried and compared with current patient data. This preliminary processing includes artifact rejection, re-referencing to common standards, and feature extraction that aligns with the current analysis pipeline, enabling seamless integration of historical patterns without adding significant complexity to the overall system architecture.
Data Source
AI summary
A computer implemented method for analyzing electroencephalogram signals can include a plurality of sensors configured to contact a skull and capture the electroencephalogram signals, one or more computer memory units for storing computer instructions and data, and one or more processors configured to perform the operations of clustering the electroencephalogram signals using at least stored objective data and added subjective data including patient profile data to provide clustered data results and predicting one or more among a medical diagnosis, assessment, plan, necessary forms, or recommendations for follow up based on the clustered data results.