EEG Classification via Deep Learning Augmentation
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Solution Overview
Problem
Conventional machine learning and deep learning techniques for early detection of Alzheimer's disease and mild cognitive impairment require significant time for preprocessing EEG signals, which is inefficient due to the use of handmade rules.
Innovation Solution
A deep learning-based method for classifying brain disorders using EEG analysis that eliminates the need for manual preprocessing by augmenting EEG data sets with techniques such as random cropping, normalization, and noise addition, and integrating age information for accurate diagnosis without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning and deep learning techniques are used for early detection of Alzheimer's disease and MCI, then classification accuracy can be achieved, but significant time is required for preprocessing EEG signals due to handmade rules
Solution Approach 1:
The system uses the raw EEG data itself to generate augmentation samples through the deep learning model, eliminating the need for external preprocessing. The model learns to classify patients with brain disorders directly from the augmented data generated by itself, creating a self-sufficient pipeline that reduces preprocessing time while maintaining classification accuracy
Solution Approach 2:
The deep learning-based screening model performs data augmentation in advance by generating synthetic EEG samples from the input data. This preliminary augmentation creates a enriched dataset that can be directly used for classification without requiring subsequent manual preprocessing steps, thereby reducing the overall processing time
2Quantity of substance
If handmade rules are used for preprocessing EEG signals to reduce the amount of primitive EEG signals, then the amount of data is reduced, but a lot of time is required in the preprocessing process
Solution Approach 1:
The patent replaces the mechanical handmade preprocessing rules with a deep learning-based automated system. The model automatically processes and augments the raw EEG data through learned transformations, substituting manual rule-based reduction with intelligent data generation that maintains data quality while reducing processing time
3Loss of time
If deep learning-based model is applied without preprocessing process through handwork, then processing time is reduced, but classification accuracy may be affected
Solution Approach 1:
The patent transforms the input EEG data by changing its parameters through deep learning-based augmentation, generating synthetic samples with varied characteristics. This parameter transformation enriches the data without requiring traditional preprocessing, allowing the model to maintain high classification accuracy while processing raw data directly
Solution Approach 2:
The system creates a composite dataset by combining the original EEG data with synthetically augmented samples generated by the deep learning model. This composite approach enriches the input data with diverse patterns, enabling accurate classification without manual preprocessing while reducing processing time
Data Source
AI summary
Disclosed are a method and an apparatus for classifying patients with brain disorders based on EEG analysis. The method for classifying patients with brain disorders based on EEG analysis includes: (a) receiving an EEG data set with a clinical diagnosis label, wherein the EEG data set includes a plurality of EEG signals and age information; (b) augmenting the EEG data set through a deep learning-based screening model, and augmenting the EEG signals and the age information in different schemes; (c) training the deep learning-based screening model to classify EEG data of patients into a target clinical diagnosis label by using the augmented EEG signals and age information; and (d) predicting a brain diagnosis label of brain disorders by applying EEG data of patients to the trained deep learning-based screening model.