Query: epilepsy

title: Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network
abstract: Epilepsy seizure prediction paves the way of timely warning for patients to take more active and effective intervention measures. Compared to seizure detection that only identifies the inter ictal state and the ictal state, far fewer researches have been conducted on seizure prediction because the high similarity makes it challenging to distinguish between the pre ictal state and the inter ictal state. In this paper, a novel solution on seizure prediction is proposed using common spatial pattern (CSP) and convolutional neural network (CNN) Firstly, artificial pre ictal EEG signals based on the original ones are generated by combining the segmented pre ictal signals to solve the trial imbalance problem between the two states. Secondly, a feature extractor employing wavelet packet decomposition and CSP is designed to extract the distinguishing features in both the time domain and the frequency domain. It can improve overall accuracy while reducing the training time. Finally, a shallow CNN is applied to discriminate between the pre ictal state and the inter ictal state. Our proposed solution is evaluated on 23 patients' data from Boston Children's Hospital MIT scalp EEG dataset by employing a leave one out cross validation, and it achieves a sensitivity of 92.2% and false prediction rate of 0.12/h. Experimental result demonstrates that the proposed approach outperforms most state of the art methods.

title: Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise
abstract: Epileptic seizure detection is traditionally performed by visual observation of Electroencephalogram (EEG) signals. Owing to its onerous and time consuming nature, seizure detection based on visual inspection hinders epilepsy diagnosis, monitoring, and large scale data analysis in epilepsy research. So, there is a dire need of an automatic seizure detection scheme.  An automated scheme for epileptic seizure identification is developed in this study. Here we utilize a signal processing technique, namely complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for epileptic seizure identification. First, we decompose segments of EEG signals into intrinsic mode functions by CEEMDAN. The mode functions are then modeled by normal inverse Gaussian (NIG) pdf parameters. In this work, NIG modeling is employed in conjunction with CEEMDAN for epileptic seizure detection for the first time. The efficacy of the NIG parameters in the CEEMDAN domain is demonstrated by intuitive, graphical, and statistical analyses. Adaptive Boosting, an eminent ensemble learning based classification model, is implemented to perform classification.  Experimental outcomes suggest that the algorithmic performance of the proposed scheme is promising in all the cases of clinical significance. Comparative evaluation of algorithmic performance with the state of the art schemes manifest that the seizure detection scheme proposed herein outperforms competing algorithms in terms of accuracy, sensitivity, specificity, and Cohen's Kappa coefficient.  Upon its implementation in clinical practice, the proposed seizure detection scheme will eliminate the onus of medical professionals and expedite epilepsy research and diagnosis.

title: Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: Application to epilepsy lesion screening
abstract: In this study, we propose a novel anomaly detection model targeting subtle brain lesions in multiparametric MRI. To compensate for the lack of annotated data adequately sampling the heterogeneity of such pathologies, we cast this problem as an outlier detection problem and introduce a novel configuration of unsupervised deep siamese networks to learn normal brain representations using a series of non pathological brain scans. The proposed siamese network, composed of stacked convolutional autoencoders as subnetworks is designed to map patches extracted from healthy control scans only and centered at the same spatial localization to 'close' representations with respect to the chosen metric in a latent space. It is based on a novel loss function combining a similarity term and a regularization term compensating for the lack of dissimilar pairs. These latent representations are then fed into oc SVM models at voxel level to produce anomaly score maps. We evaluate the performance of our brain anomaly detection model to detect subtle epilepsy lesions in multiparametric (T1 weighted, FLAIR) MRI exams considered as normal (MRI negative) Our detection model trained on 75 healthy subjects and validated on 21 epilepsy patients (with 18 MRI negatives) achieves a maximum sensitivity of 61% on the MRI negative lesions, identified among the 5 most suspicious detections on average. It is shown to outperform detection models based on the same architecture but with stacked convolutional or Wasserstein autoencoders as unsupervised feature extraction mechanisms.

title: A hybrid Local Binary Pattern and wavelets based approach for EEG classification for diagnosing epilepsy
abstract: Epilepsy is one of the grave neurological ailments affecting approximately 70 million people globally. Detection of epileptic attack is commonly carried out by viewing and analysing long duration multi channel EEG records. To counter this time consuming process, a hybrid Local Binary Pattern Wavelet based approach, classifying EEG in epileptic patients, is adopted in this research. Epilepsy is characterized by multiple ictal patterns in the form of synchronous epileptiform discharge transients. This work attempts to classify seizure from normal EEG recordings using the low frequency activity. In order to perform this classification, the EEG signal is filtered and then transformed using Local Binary Pattern (LBP) into a new signal. Discrete Wavelet Transform (DWT) is employed to decompose the obtained signal. Wavelet coefficients are calculated to 5 levels of decomposition. A combination of univariate and bivariate features forms the feature set for seizure detection. This feature set extracted from low frequency band coefficients helps in bringing out the dispersion, symmetry, and peakedness present in the EEG signal. A novel LBP based Spatio temporal analysis of the continuous EEG signal for epilepsy detection is carried out on 105 seizures from 14 randomly selected subjects of CHB MIT EEG database. A sensitivity of 100% is achieved on the CHB MIT database while long term EEG is being tested with Linear Discriminant Analysis (LDA) classifier. The algorithm works well to obtain a false detection rate (FP/Hour) of 0.59. The specificity of 99.8% is attained with a mean accuracy of 99.6% when tested on 498.9 h of EEG data.

title: Automated diagnosis of epilepsy from EEG signals using ensemble learning approach
abstract: Epilepsy is generally considered as a collection of neurological disorders manifested by epileptic seizures. It is often confirmed with an electroencephalogram (EEG) EEG signals are non stationary, nonlinear and non Gaussian. In this paper, in order to tackle this problem, we have used three different methods for feature extraction namely wavelet based entropy (approximation entropy, sample entropy, permutation entropy) nonlinear features (Hurst exponent, Higuchi Fractal Dimension) and higher order spectra (mean, normalized entropy 1 and normalized entropy 2) Further multiclass classification using indirect approach with One vs One method is employed using heterogenous ensemble learning approach. Entropy features are used for classifying normal and interictal class using k Nearest Neighbor. Higher Order Spectra features are used for classifying normal and ictal class using Support Vector Machine with Radial basis function as kernel and non linear features are used for classifying interictal and ictal class using Naive Bayes. Final verdict is taken by meta classifier with meta learning algorithm Stacking Correspondence Analysis and Nearest Neighbor (SCANN) The proposed method surpasses the existing methods in literature in terms of sensitivity and specificity.

title: Emerging roles of network analysis for epilepsy
abstract: In recent years there has been increasing interest in applying network science tools to EEG data. At the 2018 American Epilepsy Society conference in New Orleans, LA, the yearly session of the Engineering and Neurostimulation Special Interest Group focused on emerging, translational technologies to analyze seizure networks. Each speaker demonstrated practical examples of how network tools can be utilized in clinical care and provide additional data to help care for patients with intractable epilepsy. The groups presented advances using tools from functional connectivity, control theory, and graph theory to analyze human EEG data. These tools have great potential to augment clinical interpretation of EEG signals.

title: The Bayesian Virtual Epileptic Patient: A probabilistic framework designed to infer the spatial map of epileptogenicity in a personalized large scale brain model of epilepsy spread
abstract: Despite the importance and frequent use of Bayesian frameworks in brain network modeling for parameter inference and model prediction, the advanced sampling algorithms implemented in probabilistic programming languages to overcome the inference difficulties have received relatively little attention in this context. In this technical note, we propose a probabilistic framework, namely the Bayesian Virtual Epileptic Patient (BVEP) which relies on the fusion of structural data of individuals to infer the spatial map of epileptogenicity in a personalized large scale brain model of epilepsy spread. To invert the individualized whole brain model employed in this study, we use the recently developed algorithms known as No U Turn Sampler (NUTS) as well as Automatic Differentiation Variational Inference (ADVI) Our results indicate that NUTS and ADVI accurately estimate the degree of epileptogenicity of brain regions, therefore, the hypothetical brain areas responsible for the seizure initiation and propagation, while the convergence diagnostics and posterior behavior analysis validate the reliability of the estimations. Moreover, we illustrate the efficiency of the transformed non centered parameters in comparison to centered form of parameterization. The Bayesian framework used in this work proposes an appropriate patient specific strategy for estimating the epileptogenicity of the brain regions to improve outcome after epilepsy surgery.

title: Applying Deep Learning for Epilepsy Seizure Detection and Brain Mapping Visualization
abstract: Deep Convolutional Neural Network (CNN) has achieved remarkable results in computer vision tasks for end to end learning. We evaluate here the power of a deep CNN to learn robust features from raw Electroencephalogram (EEG) data to detect seizures. Seizures are hard to detect, as they vary both inter and intra patient. In this article, we use a deep CNN model for seizure detection task on an open access EEG epilepsy dataset collected at the Boston Children's Hospital. Our deep learning model is able to extract spectral, temporal features from EEG epilepsy data and use them to learn the general structure of a seizure that is less sensitive to variations. For cross patient EEG data, our method produced an overall sensitivity of 90.00% specificity of 91.65% and overall accuracy of 98.05% for the whole dataset of 23 patients. The system can detect seizures with an accuracy of 99.46% Thus, it can be used as an excellent cross patient seizure classifier. The results show that our model performs better than the previous state of the art models for patient specific and cross patient seizure detection task. The method gave an overall accuracy of 99.65% for patient specific data. The system can also visualize the special orientation of band power features. We use correlation maps to relate spectral amplitude features to the output in the form of images. By using the results from our deep learning model, this visualization method can be used as an effective multimedia tool for producing quick and relevant brain mapping images that can be used by medical experts for further investigation.

title: Implementation of Deep Neural Networks to Classify EEG Signals using Gramian Angular Summation Field for Epilepsy Diagnosis
abstract: This paper evaluates the approach of imaging timeseries data such as EEG in the diagnosis of epilepsy through Deep Neural Network (DNN) EEG signal is transformed into an RGB image using Gramian Angular Summation Field (GASF) Many such EEG epochs are transformed into GASF images for the normal and focal EEG signals. Then, some of the widely used Deep Neural Networks for image classification problems are used here to detect the focal GASF images. Three pre trained DNN such as the AlexNet, VGG16, and VGG19 are validated for epilepsy detection based on the transfer learning approach. Furthermore, the textural features are extracted from GASF images, and prominent features are selected for a multilayer Artificial Neural Network (ANN) classifier. Lastly, a Custom Convolutional Neural Network (CNN) with three CNN layers, Batch Normalization, Max pooling layer, and Dense layers, is proposed for epilepsy diagnosis from GASF images. The results of this paper show that the Custom CNN model was able to discriminate against the focal and normal GASF images with an average peak Precision of 0.885, Recall of 0.92, and F1 score of 0.90. Moreover, the Area Under the Curve (AUC) value of the Receiver Operating Characteristic (ROC) curve is 0.92 for the Custom CNN model. This paper suggests that Deep Learning methods widely used in image classification problems can be an alternative approach for epilepsy detection from EEG signals through GASF images.

title: A Unified Framework and Method for EEG Based Early Epileptic Seizure Detection and Epilepsy Diagnosis
abstract: Electroencephalogram (EEG) contains important physiological information that can reflect the activity of human brain, making it useful for epileptic seizure detection and epilepsy diagnosis. However visual inspection of large amounts of EEG by human expert is time consuming, and meanwhile there are often inconsistences in judgement between physicians. In this paper, we develop a unified framework for early epileptic seizure detection and epilepsy diagnosis, which includes two phases. In the first phase, the signal intensity is first calculated for each data point of the given EEG, enabling the well known autoregressive moving average (ARMA) model to characterize the dynamic behavior of the EEG time series. The residual error between the predicted value of learned ARMA model and the actually observed value is used as the anomaly score to support a null hypothesis testing for making epileptic seizure decision. The epileptic seizure detection phase can provide a quick detection for anomaly EEG patterns, but the resulting suspicious segment may include epilepsy or other disordering EEG activities thus required to be identified. Therefore, in the second phase, we use pattern recognition technique to classify the suspicious EEG segments. In particular, we propose a new and practical classifier based on a pairwise of one class SVMs for epilepsy diagnosis. The proposed classifier requires normal and epilepsy data for training, but can recognize normal, epilepsy and even other disorders that would not be trained in the training samples. This point is practical and meaningful in real clinic scenarios as the input EEG may include other brain disordering diseases besides of epilepsy. We conducted experiments on the publicly available Bern Barcelona and CHB MIT EEG database, respectively, to validate the effectiveness of the proposed framework, and our method achieved classification accuracy of 93% and 94% on them. Comprehensive experimental results, outperforming the state of the arts, suggest its great potentials in real applications.