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

EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population

Background: Epilepsy is one of the most common neurological conditions globally, and the fourth most common in the United States. Recurrent non-provoked seizures characterize it and have huge impacts on the quality of life and financial impacts for affected individuals. A rapid and accurate diagnosis is essential in order to instigate and monitor optimal treatments. There is also a compelling need for the accurate interpretation of epilepsy due to the current scarcity in neurologist diagnosticians and a global inequity in access and outcomes. Furthermore, the existing clinical and traditional machine learning diagnostic methods exhibit limitations, warranting the need to create an automated system using deep learning model for epilepsy detection and monitoring using a huge database. Method: The EEG signals from 35 channels were used to train the deep learning-based transformer model named (EpilepsyNet). For each training iteration, 1-min-long data were randomly sampled from each participant. Thereafter, each 5-s epoch was mapped to a matrix using the Pearson Correlation Coefficient (PCC), such that the bottom part of the triangle was discarded and only the upper triangle of the matrix was vectorized as input data. PCC is a reliable method used to measure the statistical relationship between two variables. Based on the 5 s of data, single embedding was performed thereafter to generate a 1-dimensional array of signals. In the final stage, a positional encoding with learnable parameters was added to each correlation coefficient’s embedding before being fed to the developed EpilepsyNet as input data to epilepsy EEG signals. The ten-fold cross-validation technique was used to generate the model. Results: Our transformer-based model (EpilepsyNet) yielded high classification accuracy, sensitivity, specificity and positive predictive values of 85%, 82%, 87%, and 82%, respectively. Conclusion: The proposed method is both accurate and robust since ten-fold cross-validation was employed to evaluate the performance of the model. Compared to the deep models used in existing studies for epilepsy diagnosis, our proposed method is simple and less computationally intensive. This is the earliest study to have uniquely employed the positional encoding with learnable parameters to each correlation coefficient’s embedding together with the deep transformer model, using a huge database of 121 participants for epilepsy detection. With the training and validation of the model using a larger dataset, the same study approach can be extended for the detection of other neurological conditions, with a transformative impact on neurological diagnostics worldwide

Automatic seizure detection based on Machine Learning and EEG

The diagnosis and treatment of epilepsy depend on accurate seizure detection. In clinical practice, the evaluation of seizures is done by visual inspection of an electroencephalogram (EEG). it is very time­consuming and requires trained experts. Automatic seizure detection is important. Machine learning approaches are intensely being applied to this problem due to their ability to classify seizure conditions from a large amount of data, and provide pre­screened results for neurologists. This work proposes a variety of experiments with different machine­learning architectures (support vector machine SVM, K nearest neighbour KNN, random forest RF, feef forward neural network FFNN and convolutional neural network CNN) for the detection of epileptic seizures using multichannel EEG signals from the CHBT­MIT Scalp EEG Database. The best model built in this work contains a combination of a feed­forward neural network (FFNN) and a convolutional neural network (CNN). CNN input images are constructed by applying short­time Fourier transform (STFT) to electroencephalography (EEG) signals and then merged with statistical metrics into a FFNN. The best model of this project showed an outstanding performance of 98.615% accuracy, 98.737% sensitivity and 98.425% specificity. This work also includes a discussion of other exciting ideas that could lead to future research investigations

Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy

Pattern Recognition in Brain Networks to Characterize Preictal States

La epilepsia es uno de los trastornos neurológicos más comunes que afecta a más de 50 millones de personas personas de todo el mundo. Se caracteriza por convulsiones recurrentes, que son el resultado de descargas eléctricas que generan alteraciones en la actividad cerebral. En la mayoría de los pacientes epilépticos las convulsiones son poco frecuentes y son de ocurrencia impredecible, el uso de medicamentos anticonvulsivos pueden reducir el número de incidencias de convulsiones en el paciente. Desafortunadamente, para el 30% de los pacientes con epilepsia, las convulsiones persisten a pesar del uso de este tratamiento, aumentando el riesgo de lesiones, muerte prematura y reduciendo su calidad de vida. El objetivo del presente trabajo es desarrollar una herramienta de IA específica para cada paciente. El pipeline desarrolado, integra análisis de conectividad funcional aplicado a las redes cerebrales epilépticas, para identificar con precisión el período sin convulsiones (interictal) y el intervalo de tiempo inmediatamente antes del inicio de la convulsión (estado preictal) y así, detectar un posible inicio de una convulsión. En el presente estudio, se utilizó información de 17 pacientes con epilepsia, tomados del DB "EPILEPSIAE", con registros disponibles de señal sEEG durante al menos 8-9 horas antes del inicio de la convulsión. Extracción de características, preprocesamiento de características, selección de características, aprendizaje automático y la Aplicación de Aprendizaje Profundo, y la Capacitación y Evaluación de Modelos, constituyen el principales bloques de tuberías. Se seleccionaron cinco algoritmos de Machine Learning y Deep Learning para la evaluación: Random Forest (RF), Support Vector Machines Classifier (SVC), XGBoost (XGB), redes neuronales convolucionales (CNN) y memoria a largo plazo (LSTM). Para mejorar el rendimiento de los algoritmos de clasificación a diferentes contextos, se evaluaron tres ventanas preictales de 40, 60 y 80 minutos. Un F1 puntuación superior al 60% fue alcanzada por 11/17 pacientes, con un periodo preictal de 80 minutos.Epilepsy is one of the most common neurological disorders that affects over 50 million people worldwide. It is characterized by recurrent seizures resulting from excessive electrical discharges that generate disruptions in brain activity. In most epileptic patients the seizures are infrequent and so they are of unpredictable occurrence. Therefore, antiseizure medicines can reduce the number of seizure incidences in the patient. Unfortunately, for 30% of people with epilepsy, seizures persist despite the use of this treatment, increasing the risk of injuries, and premature death and reducing the patient’s quality of life. The aim of the present work is to develop a patient-specific AI-based pipeline integrating functional connectivity analysis applied to epileptic brain networks, to accurately identify the non-seizure period (interictal) and the time interval immediately preceding the seizure onset (preictal state), and therefore detect a potential seizure onset. In the present study, 17 patients, from the EPILEPSIAE database, with recordings available for at least 8-9 hours before the seizure onset were selected. The patient’s recordings were sEEG. Feature Extraction, Feature Preprocessing, Feature Selection, Machine Learning and Deep Learning Applications, and Model training and Evaluation, constitute the main pipeline blocks. Five Machine Learning and Deep Learning algorithms were selected for evaluation: Random Forest (RF), Support Vector Machines Classifier (SVC), XGBoost (XGB), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM). To enhance the performance of the classification algorithms to different temporal contexts, three preictal windows of 40, 60, and 80 minutes were evaluated. An F1 score greater than 60% was achieved by 11/17 patients, and with the preferred preictal window among subjects being the 80-minute interval.Ingeniero (a) ElectrónicoMagíster en BioingenieríaPregrado0009-0009-1480-902

Mining Biomarkers Of Epilepsy From Large-Scale Intracranial Electroencephalography

Epilepsy is a chronic neurological disorder characterized by seizures. Affecting over 50 million people worldwide, the quality of life of a patient with uncontrolled epilepsy is degraded by medical, social, cognitive, and psychological dysfunction. Fortunately, two-thirds of these patients can achieve adequate seizure control through medications. Unfortunately, one-third cannot. Improving treatment for this patient population depends upon improving our understanding of the underlying epileptic network. Clinical therapies modulate this network to some degree of success, including surgery to remove the seizure onset zone or neuromodulation to alter the brain\u27s dynamics. High resolution intracranial EEG (iEEG) is often employed to study the dynamics of cortical networks, from interictal patterns to more complex quantitative features. These interictal patterns include epileptiform biomarkers whose detection and mapping, along with seizures and neuroimaging, form the mainstay of data for clinical decision making around drug therapy, surgery, and devices. They are also increasingly important to assess the effects of epileptic physiology on brain functions like behavior and cognition, which are not well characterized. In this work, we investigate the significance and trends of epileptiform biomarkers in animal and human models of epilepsy. We develop reliable methods to quantify interictal patterns, applying state of the art techniques from machine learning, signal processing, and EEG analysis. We then validate these tools in three major applications: 1. We study the effect of interictal spikes on human cognition, 2. We assess trends of interictal epileptiform bursts and their relationship to seizures in prolonged recordings from canines and rats, and 3. We assess the stability of long-term iEEG spanning several years. These findings have two main impacts: (1) they inform the interpretation of interictal iEEG patterns and elucidate the timescale of post-implantation changes. These findings have important implications for research and clinical care, particularly implantable devices and evaluating patients for epilepsy surgery. (2) They provide an analytical framework to enable others to mine large-scale iEEG datasets. In this way we hope to make a lasting contribution to accelerate collaborative research not only in epilepsy, but also in the study of animal and human electrophysiology in acute and chronic conditions