Abstract
Machine learning is an expanding research area. Its main application is in the medical field and particularly the detection of epilepsy and epileptic seizures through electroencephalographic signals (EEG). It aims to design an intelligent framework that enables an immediate diagnosis of this disease without neurological consultation and thus saves the lives of the epileptic patients by detecting seizures and warning them before it happens. However, as a real-time application, this kind of framework faces several challenges such as accuracy, fast responses, and optimal memory usage. Within this context, our work was carried out. We propose a new machine learning framework based on chaos and fractal theories. Two main novelties are presented in this paper. Firstly, we propose a new method for signal preprocessing, and we reconstruct new versions of studied EEG signals using derivative determination and chaotic injection. Secondly, we suggest a new method for fractal analysis using Higuchi fractal dimension (HFD). In fact, HFDs extracted from EEG derivatives lead to detect epilepsy, whereas HFDs extracted from EEG with a chaotic signal injection lead to seizure detection. In addition, feature fusion helped to linearize all classification problems. An experimental study using the Bonn EEG database proves the efficiency of our contributions in comparison to published research. An accuracy of 100% was achieved in different classification cases using few features and a simple linear classifier.
Highlights
Epilepsy is a dangerous brain disorder that can put patients’ lives at high risks
More than Higuchi fractal dimension (HFD), other nonlinear features were extracted from electroencephalographic signals (EEG) using different other signal decomposition methods, fast Fourier transform (FFT) in [4] to extract 38 features, local mean decomposition (LMD) in [7] to extract 9 features, and biorthogonal wavelet transform (BOWT) in [8] to extract 25 features. e second one is the signal derivative in the preprocessing step, which is presented in our previous work; we showed that derivatives contain important information about EEG signals
An innovative EEG signal processing method for the automatic detection of epilepsy and epileptic seizures was presented in this paper
Summary
Epilepsy is a dangerous brain disorder that can put patients’ lives at high risks. EEG is the basic examination for epilepsy therapy since it leads to significant results, is easy to achieve, and is not expensive. After that, developing automatic systems for epilepsy detection and epileptic patient monitoring became an interesting research field, with a lot of challenges for a real-time application. EEGs are complex signals due to their irregularity, nonlinearity, and nonstationarity and are obviously disturbed by noises. For all these reasons, the development of machine learning models based on the EEG is a hard task. E first one is signal preprocessing for noise removal using different filtering methods, while the second step is about feature extraction and selection to reduce the data vector using the time domain or a transformed or decomposed version of the studied signal in other domains According to a literature review, EEG processing goes through the following steps. e first one is signal preprocessing for noise removal using different filtering methods, while the second step is about feature extraction and selection to reduce the data vector using the time domain or a transformed or decomposed version of the studied signal in other domains
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