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Data mining EEG signals in depression for their diagnostic value
Mahdi Mohammadi1, Fadwa Al-Azab1, Bijan Raahemi1
1Knowledge Discovery and Data mining Lab (KDD), University of Ottawa, Ottawa, ON, Canada.
BMC Medical Informatics and Decision Making
|December 25, 2015
Summary
This study shows an 80% accuracy in differentiating major depressive disorder (MDD) patients from healthy volunteers using quantitative electroencephalogram (EEG) data. Advanced data mining techniques offer a promising tool for individual-level EEG analysis in clinical settings.
Area of Science:
- Neuroscience
- Data Science
- Medical Diagnostics
Background:
- Quantitative electroencephalogram (EEG) can differentiate major depressive disorder (MDD) patients from healthy volunteers (HV) at a group level.
- Individual-level diagnostic potential of quantitative EEG for MDD remains largely unrealized.
- Complex EEG data requires advanced mathematical models for feature pattern detection.
Purpose of the Study:
- To apply a data mining methodology for classifying EEGs of MDD patients and HVs.
- To assess the diagnostic potential of quantitative EEG for individual-level MDD detection.
Main Methods:
- Employed a data mining approach including Linear Discriminant Analysis (LDA) and Genetic Algorithm (GA) for feature reduction and selection.
- Utilized Decision Tree (DT) algorithm to build predictive models for pattern discovery.
- Evaluated models based on accuracy, sensitivity, specificity, and predictive values using EEG data from MDD patients and HVs.
Main Results:
- LDA and GA reduced utilized features by over 50%.
- Analysis of all frequency bands together yielded an 80% average classification accuracy (MDD vs. HV).
- Testing on additional data showed 80% accuracy, 70% sensitivity, 76% specificity, and predictive values around 74-75%.
Conclusions:
- The proposed automated EEG analytical approach shows potential as an adjunctive diagnostic tool.
- Findings suggest quantitative EEG analysis can aid in the clinical diagnosis of MDD at an individual level.
