An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach

Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. Several automatic techniques have been proposed using traditional approaches to assist neurologists in detecting binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal. These methods do not perform well when classifying ternary case e.g. ictal vs. normal vs. inter-ictal; the maximum accuracy for this case by the state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a system based on deep learning, which is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model, the bottleneck is the large number of learnable parameters. P-1D-CNN works on the concept of refinement approach and it results in 60% fewer parameters compared to traditional CNN models. Further to overcome the limitations of small amount of data, we proposed augmentation schemes for learning P-1D-CNN model. In almost all the cases concerning epilepsy detection, the proposed system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.Comment: 18 page

Enhanced Epileptic Seizure diagnosis using EEG Signals with Support vector machine and Bagging Classifiers

Many approaches have been proposed using Electroencephalogram (EEG) to detect epilepsy seizures in their early stages. Epilepsy seizure is a severe neurological disease. Practitioners continue to rely on manual testing of EEG signals. Artificial intelligence (AI) and Machine Learning (ML) can effectively deal with this problem. ML can be used to classify EEG signals employing feature extraction techniques. This work focuses on automated detection for epilepsy seizures using ML techniques. Various algorithms are investigated, such as  Bagging, Decision Tree (DT), Adaboost, Support vector machine (SVM), K-nearest neighbors(KNN), Artificial neural network(ANN), Naïve Bayes, and Random Forest (RF) to distinguish injected signals from normal ones with high accuracy. In this work, 54 Discrete wavelet transforms (DWTs) are used for feature extraction, and the similarity distance is applied to identify the most powerful features. The features are then selected to form the features matrix. The matrix is subsequently used to train ML. The proposed approach is evaluated through different metrics such as F-measure, precision, accuracy, and Recall. The experimental results show that the SVM and Bagging classifiers in some data set combinations, outperforming all other classifier

Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network

The Wireless Body Area Sensor Network (WBASN) is used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. One of the important applications of WBASN is patients’ healthcare monitoring of chronic diseases such as epileptic seizure. Normally, epileptic seizure data of the electroencephalograph (EEG) is captured and compressed in order to reduce its transmission time. However, at the same time, this contaminates the overall data and lowers classification accuracy. The current work also did not take into consideration that large size of collected EEG data. Consequently, EEG data is a bandwidth intensive. Hence, the main goal of this work is to design a unified compression and classification framework for delivery of EEG data in order to address its large size issue. EEG data is compressed in order to reduce its transmission time. However, at the same time, noise at the receiver side contaminates the overall data and lowers classification accuracy. Another goal is to reconstruct the compressed data and then recognize it. Therefore, a Noise Signal Combination (NSC) technique is proposed for the compression of the transmitted EEG data and enhancement of its classification accuracy at the receiving side in the presence of noise and incomplete data. The proposed framework combines compressive sensing and discrete cosine transform (DCT) in order to reduce the size of transmission data. Moreover, Gaussian noise model of the transmission channel is practically implemented to the framework. At the receiving side, the proposed NSC is designed based on weighted voting using four classification techniques. The accuracy of these techniques namely Artificial Neural Network, Naïve Bayes, k-Nearest Neighbour, and Support Victor Machine classifiers is fed to the proposed NSC. The experimental results showed that the proposed technique exceeds the conventional techniques by achieving the highest accuracy for noiseless and noisy data. Furthermore, the framework performs a significant role in reducing the size of data and classifying both noisy and noiseless data. The key contributions are the unified framework and proposed NSC, which improved accuracy of the noiseless and noisy EGG large data. The results have demonstrated the effectiveness of the proposed framework and provided several credible benefits including simplicity, and accuracy enhancement. Finally, the research improves clinical information about patients who not only suffer from epilepsy, but also neurological disorders, mental or physiological problems

Components of Soft Computing for Epileptic Seizure Prediction and Detection

Components of soft computing include machine learning, fuzzy logic, evolutionary computation, and probabilistic theory. These components have the cognitive ability to learn effectively. They deal with imprecision and good tolerance of uncertainty. Components of soft computing are needed for developing automated expert systems. These systems reduce human interventions so as to complete a task essentially. Automated expert systems are developed in order to perform difficult jobs. The systems have been trained and tested using soft computing techniques. These systems are required in all kinds of fields and are especially very useful in medical diagnosis. This chapter describes the components of soft computing and review of some analyses regarding EEG signal classification. From those analyses, this chapter concludes that a number of features extracted are very important and relevant features for classifier can give better accuracy of classification. The classifier with a suitable learning method can perform well for automated epileptic seizure detection systems. Further, the decomposition of EEG signal at level 4 is sufficient for seizure detection

Exploring machine learning techniques in epileptic seizure detection and prediction

Epilepsy is the most common neurological disorder, affecting between 0.6% and 0.8% of the global population. Among those affected by epilepsy whose primary method of seizure management is Anti Epileptic Drug therapy (AED), 30% go on to develop resistance to drugs which ultimately leads to poor seizure management. Currently, alternative therapeutic methods with successful outcome and wide applicability to various types of epilepsy are limited. During an epileptic seizure, the onset of which tends to be sudden and without prior warning, sufferers are highly vulnerable to injury, and methods that might accurately predict seizure episodes in advance are clearly of value, particularly to those who are resistant to other forms of therapy. In this thesis, we draw from the body of work behind automatic seizure prediction obtained from digitised Electroencephalography (EEG) data and use a selection of machine learning and data mining algorithms and techniques in an attempt to explore potential directions of improvement for automatic prediction of epileptic seizures. We start by adopting a set of EEG features from previous work in the field (Costa et al. 2008) and exploring these via seizure classification and feature selection studies on a large dataset. Guided by the results of these feature selection studies, we then build on Costa et al's work by presenting an expanded feature-set for EEG studies in this area. Next, we study the predictability of epileptic seizures several minutes (up to 25 minutes) in advance of the physiological onset. Furthermore, we look at the role of the various feature compositions on predicting epileptic seizures well in advance of their occurring. We focus on how predictability varies as a function of how far in advance we are trying to predict the seizure episode and whether the predictive patterns are translated across the entire dataset. Finally, we study epileptic seizure detection from a multiple-patient perspective. This entails conducting a comprehensive analysis of machine learning models trained on multiple patients and then observing how generalisation is affected by the number of patients and the underlying learning algorithm. Moreover, we improve multiple-patient performance by applying two state of the art machine learning algorithms

Classification of EEG Signals for Prediction of Epileptic Seizures

Epilepsy is a common brain disorder that causes patients to face multiple seizures in a single day. Around 65 million people are affected by epilepsy worldwide. Patients with focal epilepsy can be treated with surgery, whereas generalized epileptic seizures can be managed with medications. It has been noted that in more than 30% of cases, these medications fail to control epileptic seizures, resulting in accidents and limiting the patient’s life. Predicting epileptic seizures in such patients prior to the commencement of an oncoming seizure is critical so that the seizure can be treated with preventive medicines before it occurs. Electroencephalogram (EEG) signals of patients recorded to observe brain electrical activity during a seizure can be quite helpful in predicting seizures. Researchers have proposed methods that use machine and/or deep learning techniques to predict epileptic seizures using scalp EEG signals; however, prediction of seizures with increased accuracy is still a challenge. Therefore, we propose a three-step approach. It includes preprocessing of scalp EEG signals with PREP pipeline, which is a more sophisticated alternative to basic notch filtering. This method uses a regression-based technique to further enhance the SNR, with a combination of handcrafted, i.e., statistical features such as temporal mean, variance, and skewness, and automated features using CNN, followed by classification of interictal state and preictal state segments using LSTM to predict seizures. We train and validate our proposed technique on the CHB-MIT scalp EEG dataset and achieve accuracy of 94%, sensitivity of 93.8% , and 91.2% specificity. The proposed technique achieves better sensitivity and specificity than existing methods.publishedVersio

Performance Analysis of Deep-Learning and Explainable AI Techniques for Detecting and Predicting Epileptic Seizures

Epilepsy is one of the most common neurological diseases globally. Notably, people in low to middle-income nations could not get proper epilepsy treatment due to the cost and availability of medical infrastructure. The risk of sudden unpredicted death in Epilepsy is considerably high. Medical statistics reveal that people with Epilepsy die more prematurely than those without the disease. Early and accurately diagnosing diseases in the medical field is challenging due to the complex disease patterns and the need for time-sensitive medical responses to the patients. Even though numerous machine learning and advanced deep learning techniques have been employed for the seizure stages classification and prediction, understanding the causes behind the decision is difficult, termed a black box problem. Hence, doctors and patients are confronted with the black box decision-making to initiate the appropriate treatment and understand the disease patterns respectively. Owing to the scarcity of epileptic Electroencephalography (EEG) data, training the deep learning model with diversified epilepsy knowledge is still critical. Explainable Artificial intelligence has become a potential solution to provide the explanation and result interpretation of the learning models. By applying the explainable AI, there is a higher possibility of examining the features that influence the decision-making that either the patient recorded from epileptic or non-epileptic EEG signals. This paper reviews the various deep learning and Explainable AI techniques used for detecting and predicting epileptic seizures  using EEG data. It provides a comparative analysis of the different techniques based on their performance

Survey analysis for optimization algorithms applied to electroencephalogram

This paper presents a survey for optimization approaches that analyze and classify Electroencephalogram (EEG) signals. The automatic analysis of EEG presents a significant challenge due to the high-dimensional data volume. Optimization algorithms seek to achieve better accuracy by selecting practical features and reducing unwanted features. Forty-seven reputable research papers are provided in this work, emphasizing the developed and executed techniques divided into seven groups based on the applied optimization algorithm particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), grey wolf optimizer (GWO), Bat, Firefly, and other optimizer approaches). The main measures to analyze this paper are accuracy, precision, recall, and F1-score assessment. Several datasets have been utilized in the included papers like EEG Bonn University, CHB-MIT, electrocardiography (ECG) dataset, and other datasets. The results have proven that the PSO and GWO algorithms have achieved the highest accuracy rate of around 99% compared with other techniques