Epileptic Seizure Classification of EEGs Using Time–Frequency Analysis Based Multiscale Radial Basis Functions

Abstract

The automatic detection of epileptic seizures from electroencephalography (EEG) signals is crucial for the localization and classification of epileptic seizure activity. However, seizure processes are typically dynamic and nonstationary, and thus, distinguishing rhythmic discharges from nonstationary processes is one of the challenging problems. In this paper, an adaptive and localized time-frequency representation in EEG signals is proposed by means of multiscale radial basis functions (MRBF) and a modified particle swarm optimization (MPSO) to improve both time and frequency resolution simultaneously, which is a novel MRBF-MPSO framework of the time-frequency feature extraction for epileptic EEG signals. The dimensionality of extracted features can be greatly reduced by the principle component analysis algorithm before the most discriminative features selected are fed into a support vector machine (SVM) classifier with the radial basis function (RBF) in order to separate epileptic seizure from seizure-free EEG signals. The classification performance of the proposed method has been evaluated by using several state-of-art feature extraction algorithms and other five different classifiers like linear discriminant analysis, and logistic regression. The experimental results indicate that the proposed MRBF-MPSO-SVM classification method outperforms competing techniques in terms of classification accuracy, and shows the effectiveness of the proposed method for classification of seizure epochs and seizure-free epochs.

A framework for seizure detection using effective connectivity, graph theory, and multi-level modular network

“…In this method, information about different frequencies and different effective connectivity are combined together. In the step 1, DTF, DC, and GPDC are calculated in seven frequencies (1,4,8,12,16,20 and 24 Hz); thus, there are 7 matrices 23×23 for each of them. Then, the graph theory measures are used as features and AE implied for feature extraction from original features.…”

Cepstrum Coefficient Analysis from Low-Frequency to High-Frequency Applied to Automatic Epileptic Seizure Detection with Bio-Electrical Signals

“…The epileptic seizure-free EEG signals in datasets C and D were also produced, accordingly. Dataset E describes the epileptic seizure signals, which were collected by placing the electrodes in the epileptogenic zone, as shown in Table 1 [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][25][26][27][28][29][30][31][33][34][35][36][37][38][39][40][41][42][43]. The sample segment of each dataset is shown in Figure 1.…”

“…Since the frequency of speech signals is not linear, it requires the study of a new frequency unit that effectively represents the relationship between the size and the frequency. It must also satisfy the following three conditions: the general physical linear description, a low level of discrimination in the high frequency range, and a high level of discrimination in the low frequency range [41,43,[46][47][48][49][50][51].…”

“…This procedure is often hidden in the categorical classification. The correlation among features is a good break point to achieve the reduction of feature dimension [8,15,16,32,41,43,[52][53][54]. Recognition, also called classification, can be considered an important research field, whose common and traditional methods mainly include support vector machine (SVM), K-nearest neighbor (KNN), neural network (NN) classifiers, forest classifiers, and decision trees (DTs) [8][9][10]14,.…”

Analysis of EEG Signal Processing Techniques based on Spectrograms

“…Nevertheless, not all the information obtained is useful; it could present redundant data and increase computational time [3]. Feature extraction and selection can reduce the data dimension, in addition to showing relevant patterns of EEG signals associated with the brain activity, which may reflect good performance in classification [11,12], showing other opportunity area to research. This paper analyzes various techniques applied for processing EEG signals, showing their characteristics mainly spectrograms-based methods.…”