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Classifying Children with Learning Disabilities on the Basis of Resting State EEG Measures Using a Linear Discriminant Analysis

Published Online:September 14, 2015https://doi.org/10.1024/1016-264X/a000161

Abstract

Abstract. This study examines the usefulness of easy to obtain EEG measures to discriminate learning-disabled children (LD) from healthy control children. Here the spectral power in the delta, theta, alpha, and beta EEG bands and various power ratios (theta/alpha, theta/beta, beta/alpha, beta/theta, beta/[alpha+theta], [delta+theta]/alpha, alpha/delta, and [theta+alpha]/beta) are applied. These measures were subjected to a factor analysis with varimax rotation revealing four factors explaining 90 % of the entire variance. Factor 1 represents the power of the slow EEG frequency bands delta and theta, factor 2 the relationship between fast and slow frequency bands, factor 3 the slow to fast ratios, and factor 4 the absolute power of nearly all frequency bands. Group differences were found for three factor scores (1, 3, and 4). The linear discriminant analysis with the four factor scores as dependent and the group allocation as independent variables revealed a correct classification of 86 %. Although this classification is far from being perfect it is nevertheless reasonable high and statistically significant. Thus, EEG measures like the one used in this study might support the diagnosis of this difficult to diagnose disability. In addition, the EEG measures identified provide a deeper insight into the neural underpinnings of this disability. Based on this knowledge it might be possible to design new therapeutic strategies to treat LD.


Klassifikation von lerngestörten Kindern mit EEG-Kennwerten und einer linearen Diskriminanzanalyse

Zusammenfassung. Im Rahmen dieser Studie wird untersucht, ob relativ einfach zu messende EEG-Kennwerte genutzt werden können, um lerngestörte von unauffälligen Kindern zu unterscheiden. Als EEG-Kennwerte werden die spektrale Dichten im delta-, theta-, alpha- und beta-Band sowie verschiedene Verhältnismaße (theta/alpha, theta/beta, beta/alpha, beta/theta, beta/[alpha+theta], [delta+theta]/alpha, alpha/delta, and [theta+alpha]/beta). Diese Kennwerte wurden einer Faktoranalyse mit Varimaxrotation zugeführt, die vier Faktoren identifizierte, die 90 % der Gesamtvarianz erklärten. Faktor 1 erklärt vor allem die Varianz der Spektraldichten der langsamen EEG-Bänder, Faktor 2 die der Verhältnisse der Spektraldichten schneller zu langsamen Frequenzbändern, Faktor 3 die der Verhältnisse der Spektraldichten von langsamen zu schnellen Frequenzbändern, während Faktor 4 die Varianz der absoluten Spektraldichten in allen Frequenzbändern erklärte. Für die Faktorwerte der Faktoren 1, 3 und 4 ergaben sich signifikante Gruppenunterschiede zwischen den lerngestörten und unauffälligen Kindern. Diese Faktorwerte dieser 4 Faktoren wurden dann genutzt, um mit Hilfe einer linearen Diskriminanzanalyse lerngestörte von unauffälligen Kindern zu unterscheiden. Die Klassifikationsgenauigkeit erreichte mit 86 % eine sehr gute Trefferquote. Obwohl diese Klassifikation nicht perfekt ist, offenbaren sich diese EEG-Kennwerte als nützliche Kennwerte für die objektive Klassifizierung lerngestörter Kinder. Insofern könnten diese Kennwerte als Hilfsmittel für die Diagnose von Lernstörungen herangezogen werden. Darüber hinaus könnten diese EEG-Kennwerte auch bei der Planung von und Durchführung von Therapiemaßnahmen hilfreich sein.

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