Neural Network Based Epileptic EEG Detection and Classification

Timely diagnosis is important for saving the life of epileptic patients. In past few years, a lot of treatment are available for epilepsy. These treatments involve use of medicines. But these are not effective in controlling frequency of seizure. There is need of removal of affected region using surgery. Electroencephalogram (EEG) is a widely used technique for monitoring the brain activity and widely popular for seizure region detection. It is used before surgery for locating affected region. This manual process using EEG graphs is time consuming and requires deep expertise. In the present paper, a model has been proposed that preserves the true nature of EEG signal in form of textual one dimensional vector. The proposed model achieves a state of art performance for Bonn University dataset giving an average sensitivity, specificity of 81% and 81.4% respectively for classification among all five classes. Also for binary classification achieving 99.9%, 99.5% score value for specificity and sensitivity instead of 2D models used by other researchers. Thus developed system will significantly help neurosurgeons in increasing their performance

Epilepsy Detection using Combination DWT and Convolutional Neural Networks Based on Electroencephalogram

At the present day, smart technology has made life simpler for people in all spheres of life, including medical. It is necessary to have technology that can identify diseases or physical defects in humans since this will influence the course of therapy. One of the cutting-edge technologies used to identify epilepsy is the electroencephalogram (EEG). The signal was obtained by observed brain’s electrical activity for a period of time to get these signals. Medical professionals need to be very accurate and confident in their ability to categorize EEG patterns in order to diagnose epilepsy. This study suggested using Zero Crossing Frequency and Mean Crossing Frequency features extracted from transformed singnal using Discrete Wavelet Transform. EEG signals were classified into three categories: ictal, pre-ictal, and normal using Convolutional Neural Network. According to the study's findings, the suggested approach can accurately categorize three categories with a confidence interval (CI) of 0.0013 and an accuracy of 98.09%

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

Hybrid One-Dimensional CNN and DNN Model for Classification Epileptic Seizure

Epilepsy is a common chronic brain disease caused by abnormal neuronal activity and the occurrence of sudden or transient seizures. Electroencephalogram (EEG) is a non-invasive technique commonly used to identify epileptic brain activity. However, visual detection of the EEG is subjective, time consuming, and labour intensive for the neurologist. Therefore, we propose an automatic seizure detection using a combination of one-dimension convolution neural network (1D-CNN) with majority voting and deep neural network (DNN). EEG signals features are extracted using discrete Fourier transform (DFT) and discrete wavelet transform (DWT) which then these features will be selected with XGBoost to minimize features classified with CNN. The proposed method experimental results show that it can detect epilepsy from EEG signals perfectly with an accuracy of 100%. However, the proposed method only yielded classified EEG signals from the University of Bonn Dataset as its results

Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder

IntroductionMajor depressive disorder (MDD) is the most common mental disorder worldwide, leading to impairment in quality and independence of life. Electroencephalography (EEG) biomarkers processed with machine learning (ML) algorithms have been explored for objective diagnoses with promising results. However, the generalizability of those models, a prerequisite for clinical application, is restricted by small datasets. One approach to train ML models with good generalizability is complementing the original with synthetic data produced by generative algorithms. Another advantage of synthetic data is the possibility of publishing the data for other researchers without risking patient data privacy. Synthetic EEG time-series have not yet been generated for two clinical populations like MDD patients and healthy controls.MethodsWe first reviewed 27 studies presenting EEG data augmentation with generative algorithms for classification tasks, like diagnosis, for the possibilities and shortcomings of recent methods. The subsequent empirical study generated EEG time-series based on two public datasets with 30/28 and 24/29 subjects (MDD/controls). To obtain baseline diagnostic accuracies, convolutional neural networks (CNN) were trained with time-series from each dataset. The data were synthesized with generative adversarial networks (GAN) consisting of CNNs. We evaluated the synthetic data qualitatively and quantitatively and finally used it for re-training the diagnostic model.ResultsThe reviewed studies improved their classification accuracies by between 1 and 40% with the synthetic data. Our own diagnostic accuracy improved up to 10% for one dataset but not significantly for the other. We found a rich repertoire of generative models in the reviewed literature, solving various technical issues. A major shortcoming in the field is the lack of meaningful evaluation metrics for synthetic data. The few studies analyzing the data in the frequency domain, including our own, show that only some features can be produced truthfully.DiscussionThe systematic review combined with our own investigation provides an overview of the available methods for generating EEG data for a classification task, their possibilities, and shortcomings. The approach is promising and the technical basis is set. For a broad application of these techniques in neuroscience research or clinical application, the methods need fine-tuning facilitated by domain expertise in (clinical) EEG research

Data Augmentation for Deep-Learning-Based Electroencephalography

Background: Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)—increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc. New method: We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected? Results: DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively. Comparing with existing methods: Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8% for recombination of segmentation and 36% for noise addition and from 14% for motor imagery to 56% for mental workload—29% on average. Conclusions: DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications—if adhering to our reporting guidelines—will facilitate more detailed analysis

Data Augmentation for Deep-Learning-Based Electroencephalography

Background: Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)—increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc. New method: We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected? Results: DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively. Comparing with existing methods: Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8% for recombination of segmentation and 36% for noise addition and from 14% for motor imagery to 56% for mental workload—29% on average. Conclusions: DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications—if adhering to our reporting guidelines—will facilitate more detailed analysis

Developing artificial intelligence models for classification of brain disorder diseases based on statistical techniques

The Abstract is currently unavailable, due to the thesis being under Embargo