Predicting Epileptic Seizures: A Comprehensive Study of ML and DL Algorithms

Epilepsy, a complex neurological disorder marked by recurrent seizures, presents a formidable diagnostic and therapeutic challenge in healthcare. Electroencephalogram (EEG) signals are indispensable tools for detecting epileptic activity within the brain. Leveraging recent advancements in machine learning (ML) and deep learning(DL), Data Analytics our study investigates the effectiveness of various ML and DL algorithms for epilepsy detection using processed EEG data. Through a comprehensive literature review, we selected prominent ML and DL techniques such as Support Vector Machines (SVMs), Random Forest (RF) classifiers, Gaussian Naïve Bayes, CNNs, etc.  Our systematic experimentation and evaluation, conducted on a dataset sourced from the UCI Machine Learning Repository, demonstrates notable results achieved by the models exhibiting robust predictive capabilities. This research significantly contributes to advancing the field of epilepsy prediction, offering insights into the efficacy of diverse ML and DL models for seizure detection. The implications of these findings hold promise for refining epilepsy management strategies, ultimately enhancing patient care and quality of life. This underscores the imperative for interdisciplinary collaboration between neuroscience, AI, and healthcare to address the complex challenges posed by epilepsy.&nbsp

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

Wavelet Analysis Applied on EEG Signals for Identification of Preictal States in Epileptic Patients / Análise wavelet aplicada em sinais de EEG para identificação de estados pré-letais em pacientes epilépticos

The discrimination of the interictal and preictal states in epilepsy contributes to the construction of an efficient system of seizure prediction. Here, we performed the classification of the interictal and preictal states for EEG signals of the scalp. The energies of the levels obtained by the signal decomposition of the Wavelet Discrete Transform were used as features for classification. The kNN and SVM classifiers were used in the analysis of the individual EEG channels, which gave indications that the occipital lobe region channels are the most relevant to differentiate between the interictal and preictal states. Using these channels, the classification into two states achieved accuracy of 97.29%, sensitivity of 96.25% and specificity of 98.33%. In addition, the different frequency ranges obtained by Wavelet for the classification were analyzed, and it was observed that the range of 32 Hz to 128 Hz presented greater relevance in the task

Epileptic seizure detection from electroencephalogram (EEG) signals using linear graph convolutional network and DenseNet based hybrid framework

A clinical condition known as epilepsy occurs when the brain's regular electrical activity is disturbed, resulting in a rapid, aberrant, and excessive discharge of brain neurons. The electroencephalogram (EEG) signal is the measurement of electrical activity received from the nerve cells of the cerebral cortex to make precise diagnoses of disorders, which is made crucial attention for treating epilepsy patients in recent years. The concentration on grid-like data has been a significant drawback of existing deep learning-based automatic epileptic seizure detection algorithms from raw EEG signals; nevertheless, physiological recordings frequently have irregular and unordered structures, making it challenging to think of them as a matrix. In order to take advantage of the implicit information that exists in seizure detection, graph neural networks have received a lot of attention. These networks feature interacting nodes connected by edges whose weights can be either dictated by temporal correlations or anatomical junctions. To address this limitation, a novel hybrid framework is proposed for epileptic seizure detection by using linear graph convolution neural network (LGCN) and DenseNet. When compared to previous deep learning networks, DenseNet achieves the model's higher computational accuracy and memory efficiency by reducing the vanishing gradient problem and enhancing feature propagation in each of its layers. The Stockwell transform (S-transform) is used to preprocess from the raw EEG signal and then group the resulting matrix into time-frequency blocks as inputs for the LGCN to use for feature selection and after the Densenet uses for classification. The proposed hybrid framework outperforms the state-of-the-art in seizure detection tasks, achieving 98% accuracy and 98.60% specificity in extensive experiments on the publicly available CHB-MIT EEG dataset

Epileptic seizure detection and prediction based on EEG signal

Epilepsy is a kind of chronic brain disfunction, manifesting as recurrent seizures which is caused by sudden and excessive discharge of neurons. Electroencephalogram (EEG) recordings is regarded as the golden standard for clinical diagnosis of epilepsy disease. The diagnosis of epilepsy disease by professional doctors clinically is time-consuming. With the help artificial intelligence algorithms, the task of automatic epileptic seizure detection and prediction is called a research hotspot. The thesis mainly contributes to propose a solution to overfitting problem of EEG signal in deep learning and a method of multiple channels fusion for EEG features. The result of proposed method achieves outstanding performance in seizure detection task and seizure prediction task. In seizure detection task, this paper mainly explores the effect of the deep learning in small data size. This thesis designs a hybrid model of CNN and SVM for epilepsy detection compared with end-to-end classification by deep learning. Another technique for overfitting is new EEG signal generation based on decomposition and recombination of EEG in time-frequency domain. It achieved a classification accuracy of 98.8%, a specificity of 98.9% and a sensitivity of 98.4% on the classic Bonn EEG data. In seizure prediction task, this paper proposes a feature fusion method for multi-channel EEG signals. We extract a three-order tensor feature in temporal, spectral and spatial domain. UMLDA is a tensor-to-vector projection method, which ensures minimal redundancy between feature dimensions. An excellent experimental result was finally obtained, including an average accuracy of 95%, 94% F1-measure and 90% Kappa index

Network resilience

Many systems on our planet are known to shift abruptly and irreversibly from one state to another when they are forced across a "tipping point," such as mass extinctions in ecological networks, cascading failures in infrastructure systems, and social convention changes in human and animal networks. Such a regime shift demonstrates a system's resilience that characterizes the ability of a system to adjust its activity to retain its basic functionality in the face of internal disturbances or external environmental changes. In the past 50 years, attention was almost exclusively given to low dimensional systems and calibration of their resilience functions and indicators of early warning signals without considerations for the interactions between the components. Only in recent years, taking advantages of the network theory and lavish real data sets, network scientists have directed their interest to the real-world complex networked multidimensional systems and their resilience function and early warning indicators. This report is devoted to a comprehensive review of resilience function and regime shift of complex systems in different domains, such as ecology, biology, social systems and infrastructure. We cover the related research about empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also discuss some ambiguous definitions, such as robustness, resilience, and stability.Comment: Review chapter

Classification and detection of Critical Transitions: from theory to data

From population collapses to cell-fate decision, critical phenomena are abundant in complex real-world systems. Among modelling theories to address them, the critical transitions framework gained traction for its purpose of determining classes of critical mechanisms and identifying generic indicators to detect and alert them (“early warning signals”). This thesis contributes to such research field by elucidating its relevance within the systems biology landscape, by providing a systematic classification of leading mechanisms for critical transitions, and by assessing the theoretical and empirical performance of early warning signals. The thesis thus bridges general results concerning the critical transitions field – possibly applicable to multidisciplinary contexts – and specific applications in biology and epidemiology, towards the development of sound risk monitoring system