Detection and Classification of EEG Epileptiform Transients with RBF Networks using Hilbert Huang Transform-derived Features

Diagnosis of epilepsy or epileptic transients AEP (Abnormal Epileptiform Paroxysmal) is tedious, but important, and an expensive process. The process involves trained neurologists going over the patient\u27s EEG records looking for epileptiform discharge like events and classifying it as AEP (Abnormal Epileptiform Paroxysmal) or non-AEP. The objective of this research is to automate the process of detecting such events and classifying them into AEP(definitely an Epileptiform Transient) and non-AEPs (unlikely an epileptiform transient). The problem is approached in two separate steps and cascaded to validate and analyze the performance of the overall system. The first step is a detection problem to find the Epileptiform like transients (ETs) from the Electroencephalograph (EEG) of a patient. A Radial basis function-based neural network has been trained using a training set consisting of examples from both classes (ETs and non-ETs). The ETs are the yellow boxes which are marked by expert neurologists. There are no particular examples of non-ETs and any data not annotated by experts can be considered to be examples of non-ETs. The second step is classification of the detected ETs also known as yellow boxes, into AEPs or non-AEPs. A similar Radial basis function-based neural network has been trained using the ETs marked and classified into AEPs and non-AEPs manually by seven expert neurologists. The annotations or yellow boxes along with the contextual signal was used to extract features using the Hilbert Huang Transform. The system is validated by considering an entire epoch of the patient EEG and potential ETs are identified using the detector. The potential ETs marked by the detector are classified into AEPs and non-AEPs and compared against the annotations marked by the experts

Studying the Use of Hidden Markov Models in the Detection and Classification of EEG Epileptiform Transients using LPC features

The process of identifying the presence of an AEP (Abnormal Epileptiform Paroxysmal) in a subject\u27s EEG, normally done by neurologist experts, is a particularly long one and involves considerable financial expenses. This research aims to pave an automatic method of detecting and classifying streams of EEGs as to whether or not it has any AEPs present in it. This is a two step process, where step 1 is the classification problem and step 2 is the detection problem. There are many different activities on the EEGs, and the classification task helps to identify which of these activities are AEPs. So, this task involves training 2 HMMs to classify all given artifacts into 2 classes, AEP or NonAEP. LPC features extracted from the spike have been used to train the HMMs. The detection task is to find out the presence of ETs (Epileptiform Transients) from a patient\u27s EEG. For detection, two HMMs have been trained on examples taken from two classes, the ETs and the Non-ETs. The ETs class is all the Yellow Boxed annotations provided to us by the experts. The Non-ET class data has been formed by taking into consideration all the data which has not been marked as an ET. In this task, LPC features extracted from the spike and the contextual information has seen to provide good results. For validation of the system, a cascaded structure of four HMMs is formed. The first two HMMs are for detection and the next two classify the detected ETs. Test EEG signals, having both AEPs and NonAEPs are passed through this system, and the AEPs are marked and identied. The results have been compared to the annotations marked by experts

A hybrid unsupervised approach toward EEG epileptic spikes detection

Epileptic spikes are complementary sources of information in EEG to diagnose and localize the origin of epilepsy. However, not only is visual inspection of EEG labor intensive, time consuming, and prone to human error, but it also needs long-term training to acquire the level of skill required for identifying epileptic discharges. Therefore, computer-aided approaches were employed for the purpose of saving time and increasing the detection and source localization accuracy. One of the most important artifacts that may be confused as an epileptic spike, due to morphological resemblance, is eye blink. Only a few studies consider removal of this artifact prior to detection, and most of them used either visual inspection or computer-aided approaches, which need expert supervision. Consequently, in this paper, an unsupervised and EEG-based system with embedded eye blink artifact remover is developed to detect epileptic spikes. The proposed system includes three stages: eye blink artifact removal, feature extraction, and classification. Wavelet transform was employed for both artifact removal and feature extraction steps, and adaptive neuro-fuzzy inference system for classification purpose. The proposed method is verified using a publicly available EEG dataset. The results show the efficiency of this algorithm in detecting epileptic spikes using low-resolution EEG with least computational complexity, highest sensitivity, and lesser human interaction compared to similar studies. Moreover, since epileptic spike detection is a vital component of epilepsy source localization, therefore this algorithm can be utilized for EEG-based pre-surgical evaluation of epilepsy

A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and non-epileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that 1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and 2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763)

A Study of Automatic Detection and Classification of EEG Epileptiform Transients

This Dissertation documents methods for automatic detection and classification of epileptiform transients, which are important clinical issues. There are two main topics: (1) Detection of paroxysmal activities in EEG; and (2) Classification of paroxysmal activities. This machine learning algorithms were trained on expert opinion which was provided as annotations in clinical EEG recordings, which are called \u27yellow boxes\u27 (YBs). The Dissertation describes improved wavelet-based features which are used in machine learning algorithms to detect events in clinical EEG. It also reveals the influence of electrode positions and cardinality of datasets on the outcome. Furthermore, it studies the utility of using fuzzy strategies to obtain better performance than using crisp decision strategies. In the yellow-box detection study, this Dissertation makes use of threshold strategies and implementation of ANNs. It develops two types of features, wavelet and morphology, for comparison. It also explores the possibility to reduce input vector dimension by pruning. A full-scale real-time simulation of YB detection is performed. The simulation results are demonstrated using a web-based EEG viewing system designed in the School of Computing at Clemson, called EEGnet. Results are compared to expert marked YBs

Wearable electroencephalography for long-term monitoring and diagnostic purposes

Truly Wearable EEG (WEEG) can be considered as the future of ambulatory EEG units, which are the current standard for long-term EEG monitoring. Replacing these short lifetime, bulky units with long-lasting, miniature and wearable devices that can be easily worn by patients will result in more EEG data being collected for extended monitoring periods. This thesis presents three new fabricated systems, in the form of Application Specific Integrated Circuits (ASICs), to aid the diagnosis of epilepsy and sleep disorders by detecting specific clinically important EEG events on the sensor node, while discarding background activity. The power consumption of the WEEG monitoring device incorporating these systems can be reduced since the transmitter, which is the dominating element in terms of power consumption, will only become active based on the output of these systems. Candidate interictal activity is identified by the developed analog-based interictal spike selection system-on-chip (SoC), using an approximation of the Continuous Wavelet Transform (CWT), as a bandpass filter, and thresholding. The spike selection SoC is fabricated in a 0.35 μm CMOS process and consumes 950 nW. Experimental results reveal that the SoC is able to identify 87% of interictal spikes correctly while only transmitting 45% of the data. Sections of EEG data containing likely ictal activity are detected by an analog seizure selection SoC using the low complexity line length feature. This SoC is fabricated in a 0.18 μm CMOS technology and consumes 1.14 μW. Based on experimental results, the fabricated SoC is able to correctly detect 83% of seizure episodes while transmitting 52% of the overall EEG data. A single-channel analog-based sleep spindle detection SoC is developed to aid the diagnosis of sleep disorders by detecting sleep spindles, which are characteristic events of sleep. The system identifies spindle events by monitoring abrupt changes in the input EEG. An approximation of the median frequency calculation, incorporated as part of the system, allows for non-spindle activity incorrectly identified by the system as sleep spindles to be discarded. The sleep spindle detection SoC is fabricated in a 0.18 μm CMOS technology, consuming only 515 nW. The SoC achieves a sensitivity and specificity of 71.5% and 98% respectively.Open Acces

Patient-specific seizure onset detection

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 121-124).Approximately one percent of the world's population exhibits symptoms of epilepsy, a serious disorder of the central nervous system that predisposes those affected to experiencing recurrent seizures. The risk of injury associated with epileptic seizures might be mitigated by the use of a device that can reliably detect or predict the onset of seizure episodes and then warn caregivers of the event. In a hospital this device could also be used to initiate time-sensitive clinical procedures necessary for characterizing epileptic syndromes. This thesis discusses the design of a real-time, patient-specific method that can be used to detect the onset of epileptic seizures in non-invasive EEG, and then initiate time-sensitive clinical procedures like ictal SPECT. We adopt a patient-specific approach because of the clinically observed consistency of seizure and non-seizure EEG characteristics within patients, and their great heterogeneity across patients. We also treat patient-specific seizure onset detection as a binary classification problem. Our observation is a multi-channel EEG signal; its features include amplitude, fundamental frequency, morphology, and spatial localization on the scalp; and it is classified as an instance of non-seizure or seizure EEG based on the learned features of training examples from a single patient. We use a multi-level wavelet decomposition to extract features that capture the amplitude, fundamental frequency, and morphology of EEG waveforms. These features are then classified using a support vector machine or maximum-likelihood classifier trained on a patient's seizure and non-seizure EEG; non-seizure EEG includes normal and artifact contaminated EEG from various states of consciousness.(cont.) The outcome of the classification is examined in the context of automatically extracted spatial and temporal constraints before the onset of seizure activity is declared. During validation tests our method exhibited an average latency of 8.0[plus-minus]3.2 seconds while correctly identifying 131 of 139 seizure events from thirty-six, de-identified test subjects; and only 11 false-detections over 49 hours of randomly selected non-seizure EEG from these subjects. The validation tests also highlight the high learning rate of the detector; a property that allows it to exhibit excellent performance even when trained on as few as two seizure events from the test subject. We also demonstrate through a comparative study that our patient-specific detector outperforms a nonpatient-specific, or generic detector in terms of a lower average detection latency; a lower total number of false-detections; and a higher total number of true-detections. Our study also underscores the likely event of a generic detector performing very poorly when the seizure EEG of a subject in its training set matches the non-seizure EEG of the test subject.by Ali Hossam Shoeb.M.Eng

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer