Towards Interpretable Machine Learning in EEG Analysis

Studies in health technology and informatics, vol. 283, pp. 32–38

Abstract

In this paper a machine learning model for automatic detection of abnormalities in electroencephalography (EEG) is dissected into parts, so that the influence of each part on the classification accuracy score can be examined. The most successful setup of several shallow artificial neural networks aggregated via voting results in accuracy of 81%. Stepwise simplification of the model shows the expected decrease in accuracy, but a naive model with thresholding of a single extracted feature (relative wavelet energy) is still able to achieve 75%, which remains strongly above the random guess baseline of 54%. These results suggest the feasibility of building a simple classification model ensuring accuracy scores close to the state-of-the-art research but remaining fully interpretable.

Authors 3

1. Maged Mortaga Aachen Institute of Medical Informatics Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany

2. Alexander Brenner

   University of Münster

   Affiliation as printed

   Institute of Medical Informatics, University of Münster, Münster, Germany

3. Ekaterina Kutafina corresponding Aachen Institute of Medical Informatics Medical Faculty

   RWTH Aachen University · AGH University of Krakow

   Affiliation as printed

   Faculty of Applied Mathematics, AGH University of Science and Technology, Krakow, Poland

   Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany