Name
Title | ||
|---|---|---|
Binary And Multiclass Classifiers Based On Multitaper Spectral Features For Epilepsy Detection |
Abstract | ||
|---|---|---|
Epilepsy is one of the most common neurological disorders that can be diagnosed by means of electroencephalogram (EEG) analysis, 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 employing binary and multiclass classifiers. For feature extraction, a total of 105 measurements were extracted from power spectrum, spectrogram, and bispectrogram. For classifier building, widely known machine learning algorithms were used. Our method was applied in a publicly available EEG database. As a result, BP-MLP (backpropagation based on multilayer perceptron) and SMO_Pol (sequential minimal optimization supported by the polynomial kernel) algorithms reached the highest accuracy for binary (100%) and multiclass (98%) classification problems. Subsequently, statistical tests did not find a better performance model. In the evaluation based on confusion matrices, it was also impossible to identify a classifier that stands out concerning other models for EEG classification. In comparison to related words, our predictive models reached competitive results. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.bspc.2021.102469 | BIOMEDICAL SIGNAL PROCESSING AND CONTROL |
Keywords | DocType | Volume |
Electroencephalogram, Epilepsy, Signal processing, Spectral features, Machine learning, Multiclass classification | Journal | 66 |
ISSN | Citations | PageRank |
1746-8094 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oliva Jefferson Tales | 1 | 0 | 0.34 |
| Rosa João Luís Garcia | 2 | 0 | 0.34 |