Automatic Diagnosis of Epileptic Seizures in EEG Signals Using Fractal Dimension Features and Convolutional Autoencoder Method

Abstract

This paper proposes a new method for epileptic seizure detection in electroencephalography (EEG) signals using nonlinear features based on fractal dimension (FD) and a deep learning (DL) model. Firstly, Bonn and Freiburg datasets were used to perform experiments. The Bonn dataset consists of binary and multi-class classification problems, and the Freiburg dataset consists of two-class EEG classification problems. In the preprocessing step, all datasets were prepossessed using a Butterworth band pass filter with 0.5–60 Hz cut-off frequency. Then, the EEG signals of the datasets were segmented into different time windows. In this section, dual-tree complex wavelet transform (DT-CWT) was used to decompose the EEG signals into the different sub-bands. In the following section, in order to feature extraction, various FD techniques were used, including Higuchi (HFD), Katz (KFD), Petrosian (PFD), Hurst exponent (HE), detrended fluctuation analysis (DFA), Sevcik, box counting (BC), multiresolution box-counting (MBC), Margaos-Sun (MSFD), multifractal DFA (MF-DFA), and recurrence quantification analysis (RQA). In the next step, the minimum redundancy maximum relevance (mRMR) technique was used for feature selection. Finally, the k-nearest neighbors (KNN), support vector machine (SVM), and convolutional autoencoder (CNN-AE) were used for the classification step. In the classification step, the K-fold cross-validation with k = 10 was employed to demonstrate the effectiveness of the classifier methods. The experiment results show that the proposed CNN-AE method achieved an accuracy of 99.736% and 99.176% for the Bonn and Freiburg datasets, respectively.

Challenges and perspectives in recurrence analyses of event time series

“…RPs of event series can be used in a similar manner for such kind of classification tasks. Other characteristics of event series (like serial dependence) would be accessible to machine learning approaches by the RQA measures [61][62][63] .…”

Trends in recurrence analysis of dynamical systems

“…12B). Features based on RQA measures are meanwhile frequently used for classification purposes using SVMs, CNNs, k -nearest neighbour or random forest classifications [118][119][120][121][122][123][124] and the ML-toolbox offers a variety of other methods for clustering and feature classification (Fig. 12B).…”

Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals

“…Long seizure activity can last up to 15 min, while short seizure activity lasts only 12 s. Experts use the clinical presentation of epilepsy patients to mark when each seizure event begins and ends. In total, this dataset contains 87 seizure events, and the mean seizure duration is 114.3 s. More detailed data about the Freiburg dataset can be found in reference [ 33 ]. In our experiments, one or more seizure events and same amount of nonseizure EEG data were randomly selected for each patient for training, and the rest of the EEG data were used to test the trained model.…”