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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.

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

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.