Binary and Multiclass Classifiers based on Multitaper Spectral Features for Epilepsy Detection

• URL: http://arxiv.org/abs/2004.03456v1
• Date: Thu, 2 Apr 2020 17:12:33 GMT
• Title: Binary and Multiclass Classifiers based on Multitaper Spectral Features for Epilepsy Detection
• Authors: Jefferson Tales Oliva and Jo\~ao Lu\'is Garcia Rosa
• Abstract summary: Epilepsy is one of the most common neurological disorders that can be diagnosed through electroencephalogram (EEG) We present a novel method for epilepsy detection into two differentiation contexts: binary and multiclass classification.
• Score: 1.721147796970279
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Epilepsy is one of the most common neurological disorders that can be diagnosed through electroencephalogram (EEG), in which the following epileptic events can be observed: pre-ictal, ictal, post-ictal, and interictal. In this paper, we present a novel method for epilepsy detection into two differentiation contexts: binary and multiclass classification. For feature extraction, a total of 105 measures were extracted from power spectrum, spectrogram, and bispectrogram. For classifier building, eight different machine learning algorithms were used. Our method was applied in a widely used EEG database. As a result, random forest and backpropagation based on multilayer perceptron algorithms reached the highest accuracy for binary (98.75%) and multiclass (96.25%) classification problems, respectively. Subsequently, the statistical tests did not find a model that would achieve a better performance than the other classifiers. In the evaluation based on confusion matrices, it was also not possible to identify a classifier that stands out in relation to other models for EEG classification. Even so, our results are promising and competitive with the findings in the literature.

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