Abstract

Analysis of electroencephalogram (EEG) signals is essential because it is an efficient method to diagnose neurological brain disorders. In this work, a single system is developed to diagnose one or two neurological diseases at the same time (two-class mode and three-class mode). For this purpose, different EEG feature-extraction and classification techniques are investigated to aid in the accurate diagnosis of neurological brain disorders: epilepsy and autism spectrum disorder (ASD). Two different modes, single-channel and multi-channel, of EEG signals are analyzed for epilepsy and ASD. The independent components analysis (ICA) technique is used to remove the artifacts from EEG dataset. Then, the EEG dataset is segmented and filtered to remove noise and interference using an elliptic band-pass filter. Next, the EEG signal features are extracted from the filtered signal using a discrete wavelet transform (DWT) to decompose the filtered signal to its sub-bands delta, theta, alpha, beta and gamma. Subsequently, five statistical methods are used to extract features from the EEG sub-bands: the logarithmic band power (LBP), standard deviation, variance, kurtosis, and Shannon entropy (SE). Further, the features are fed into four different classifiers, linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural networks (ANNs), to classify the features corresponding to their classes. The combination of DWT with SE and LBP produces the highest accuracy among all the classifiers. The overall classification accuracy approaches 99.9% using SVM and 97% using ANN for the three-class single-channel and multi-channel modes, respectively.

Highlights

  • The electroencephalogram (EEG) signals reflect the electrical activities of brain behaviors.The signal-processing techniques based on EEG signals analysis form an important clinical tool for monitoring and diagnosing neurological brain disorders such as autism spectrum disorder (ASD) and epilepsy disorders because they reflect the electrical activities or disorders of neurons in the human brain.Brain disorders, such as ASD and epilepsy disorders, are defined by such activities in the human brain.Currently, most brain disorder diagnoses are performed manually by neurologists or skilled cliniciansSensors 2020, 20, 2505; doi:10.3390/s20092505 www.mdpi.com/journal/sensorsSensors 2020, 20, 2505 through visual inspection of EEG signals

  • The features were extracted from the EEG signal frequency bands; delta, theta, alpha, beta, and gamma using discrete wavelet transform (DWT) combined with the logarithmic band power (LBP), standard deviation (SD), variance, kurtosis, and entropy methods

  • EEG signal-analysis techniques have been improved in recent years because the EEG reflects neurological brain activity and is an important tool for diagnosing neurological brain disorders, such as autism, epilepsy, and Alzheimer’s disease

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Summary

Introduction

The electroencephalogram (EEG) signals reflect the electrical activities of brain behaviors.The signal-processing techniques based on EEG signals analysis form an important clinical tool for monitoring and diagnosing neurological brain disorders such as autism spectrum disorder (ASD) and epilepsy disorders because they reflect the electrical activities or disorders of neurons in the human brain.Brain disorders, such as ASD and epilepsy disorders, are defined by such activities in the human brain.Currently, most brain disorder diagnoses are performed manually by neurologists or skilled cliniciansSensors 2020, 20, 2505; doi:10.3390/s20092505 www.mdpi.com/journal/sensorsSensors 2020, 20, 2505 through visual inspection of EEG signals. The electroencephalogram (EEG) signals reflect the electrical activities of brain behaviors. The signal-processing techniques based on EEG signals analysis form an important clinical tool for monitoring and diagnosing neurological brain disorders such as autism spectrum disorder (ASD) and epilepsy disorders because they reflect the electrical activities or disorders of neurons in the human brain. Brain disorders, such as ASD and epilepsy disorders, are defined by such activities in the human brain. The human brain is the most complex part of the human body and provides a wide variety of information related to limbic movements and neurological disorders. Researchers in multidisciplinary fields of engineering, neuroscience, microelectronics, bioengineering, and neurophysiology have attempted to take advantage of the information provided by EEG signals for several application domains, such as controls, communications, and medical diagnosis

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