Delay Differential Analysis of Seizures in Multichannel Electrocorticography Data

High-density electrocorticogram (ECoG) electrodes are capable of recording neurophysiological data with high temporal resolution with wide spatial coverage. These recordings are a window to understanding how the human brain processes information and subsequently behaves in healthy and pathologic states. Here, we describe and implement delay differential analysis (DDA) for the characterization of ECoG data obtained from human patients with intractable epilepsy. DDA is a time-domain analysis framework based on embedding theory in nonlinear dynamics that reveals the nonlinear invariant properties of an unknown dynamical system. The DDA embedding serves as a low-dimensional nonlinear dynamical basis onto which the data are mapped. This greatly reduces the risk of overfitting and improves the method's ability to fit classes of data. Since the basis is built on the dynamical structure of the data, preprocessing of the data (e.g., filtering) is not necessary. We performed a large-scale search for a DDA model that best fit ECoG recordings using a genetic algorithm to qualitatively discriminate between different cortical states and epileptic events for a set of 13 patients. A single DDA model with only three polynomial terms was identified. Singular value decomposition across the feature space of the model revealed both global and local dynamics that could differentiate electrographic and electroclinical seizures and provided insights into highly localized seizure onsets and diffuse seizure terminations. Other common ECoG features such as interictal periods, artifacts, and exogenous stimuli were also analyzed with DDA. This novel framework for signal processing of seizure information demonstrates an ability to reveal unique characteristics of the underlying dynamics of the seizure and may be useful in better understanding, detecting, and maybe even predicting seizures

An adaptive filtering approach using supervised SSA for identification of sleep stages from EEG

Purpose: Sleep is a complex physiological state and an indicator of the changes in the brain function similar to those occurring in many psychiatric and neurological conditions. Since visual sleep scoring consuming process, automatic sleep staging methods, also called scoring, hold promise in diagnosing alterations in the sleep process and the sleep EEG more effectively. Method: In this paper, a supervised approach for sleep scoring from single channel EEG signals is proposed. First, a supervised singular spectrum analysis (SSA) which is a subspace based method is used to extract the desired signal for each stage of sleep. Then, two recursive least squares (RLS) adaptive filters are trained and used to identify first and deep sleep stages. Result: The proposed system which can be considered as a filter bank for separating multiple signal subbands is tested using real EEG where the results verify the accuracy of the proposed method. Conclusion: The overall result show the effectiveness of algorithm for detection of sleep stages from EEG signals often characterised by a sharp increase in delta and a rapid decrease in alpha as sleep deepens

Investigation and Modelling of Fetal Sheep Maturation

In this thesis, I study the maturational changes of the fetal sheep ECoG (electrocorticogram) in its third-trimester of gestation (95-140 days of gestation), investigate three continuum models for electrical behaviour of the cortex, and tune the parameters in one of these models to generate the discontinuous EEG waves in the immature cortex. Visual inspection of the ECoG time-series shows that the third-trimester of fetal sheep is comprised of two stages: early third-trimester characterised by bursting activity separated by silent intervals, and late third-trimester with well-defined SWS (slow wave sleep) and REM (rapid eye movement) sleep states. For the late third-trimester, the results of power, correlation time, and SVD (singular value decomposition) entropy analysis demonstrate that the sleep state change is a cortical phase transition—with SWS-to-REM transition being a first-order transition, and REM-to-SWS second-order. Further analyses by correlation time, SVD entropy, and spectral edge frequency display that the differentiation of the two distinct SWS and REM sleep states occurs at about 125 dGA (day gestational age). Spectral analysis divides the third-trimester into four stages in terms of the frequency and amplitude variations of the major resonances. Spindle-like resonances only occur in the first stage. A power surge is observed immediately prior to the emergence of the two sleep states. Most significant changes of the spectrum occur during the fourth stage for both SWS (in amplitude) and REM (in frequency) sleep states. For the modelling of the immature cortex, different theoretical descriptions of cortical behaviour are investigated, including the ccf (cortical column field) model of J. J. Wright, and the Waikato cortical model. For the ccf model at centimetric scale, the time-series, fluctuation power, power law relation, gamma oscillation, phase relation between excitatory and inhibitory elements, power spectral density, and spatial Fourier spectrum are quantified from numerical simulations. From these simulations, I determined that the physiologically sophisticated ccf model is too large and unwieldy for easy tuning to match the electrical response of the immature cortex. The Waikato near-far fast-soma model is constructed by incorporating the back-propagation effect of the action potential into the Waikato fast-soma model, state equations are listed and stability prediction are performed by varying the gap junction diffusion strength, subcortical drive, and the rate constants of the near- and far-dendritic tree. In the end, I selected the classic and simpler Waikato slow-soma mean-field model to use for my immature cortex simulations. Model parameters are customised based on the physiology of the immature cortex, including GABA (an inhibitory neurotransmitter in adult) excitatory effect, number of synaptic connections, and rate constants of the IPSPs (inhibitory postsynaptic potential). After hyperpolarising the neuron resting voltage sufficiently to cause the immature inhibitory neuron to act as an excitatory agent, I alter the rate constant of the IPSP, and study the stability of the immature cortex. The bursting activity and quiet states of the discontinuous EEG are simulated and the gap junction diffusion effect in the immature cortex is also examined. For a rate constant of 18.6 s-1, slow oscillations in the quiet states are generated, and for rate constant of 25 s-1, a possible cortical network oscillation emerges. As far as I know, this is the first time that the GABA excitatory effect has been integrated into a mean-field cortical model and the discontinuous EEG wave successfully simulated in a qualitative way

Analysis of Signal Decomposition and Stain Separation methods for biomedical applications

Nowadays, the biomedical signal processing and classification and medical image interpretation play an essential role in the detection and diagnosis of several human diseases. The problem of high variability and heterogeneity of information, which is extracted from digital data, can be addressed with signal decomposition and stain separation techniques which can be useful approaches to highlight hidden patterns or rhythms in biological signals and specific cellular structures in histological color images, respectively. This thesis work can be divided into two macro-sections. In the first part (Part I), a novel cascaded RNN model based on long short-term memory (LSTM) blocks is presented with the aim to classify sleep stages automatically. A general workflow based on single-channel EEG signals is developed to enhance the low performance in staging N1 sleep without reducing the performances in the other sleep stages (i.e. Wake, N2, N3 and REM). In the same context, several signal decomposition techniques and time-frequency representations are deployed for the analysis of EEG signals. All extracted features are analyzed by using a novel correlation-based timestep feature selection and finally the selected features are fed to a bidirectional RNN model. In the second part (Part II), a fully automated method named SCAN (Stain Color Adaptive Normalization) is proposed for the separation and normalization of staining in digital pathology. This normalization system allows to standardize digitally, automatically and in a few seconds, the color intensity of a tissue slide with respect to that of a target image, in order to improve the pathologist’s diagnosis and increase the accuracy of computer-assisted diagnosis (CAD) systems. Multiscale evaluation and multi-tissue comparison are performed for assessing the robustness of the proposed method. In addition, a stain normalization based on a novel mathematical technique, named ICD (Inverse Color Deconvolution) is developed for immunohistochemical (IHC) staining in histopathological images. In conclusion, the proposed techniques achieve satisfactory results compared to state-of-the-art methods in the same research field. The workflow proposed in this thesis work and the developed algorithms can be employed for the analysis and interpretation of other biomedical signals and for digital medical image analysis

Quaternion singular spectrum analysis of electroencephalogram With application in sleep analysis

A novel quaternion-valued singular spectrum analysis (SSA) is introduced for multichannel analysis of electroencephalogram (EEG). The analysis of EEG typically requires the decomposition of data channels into meaningful components despite the notoriously noisy nature of EEG - which is the aim of SSA. However, the singular value decomposition involved in SSA implies the strict orthogonality of the decomposed components, which may not reflect accurately the sources which exhibit similar neural activities. To allow for the modelling of such co-channel coupling, the quaternion domain is considered for the first time to formulate the SSA using the augmented statistics. As an application, we demonstrate how the augmented quaternion-valued SSA (AQSSA) can be used to extract the sources, even at a signal-to-noise ratio as low as -10 dB. To illustrate the usefulness of our quaternion-valued SSA in a rehabilitation setting, we employ the proposed SSA for sleep analysis to extract statistical descriptors for five-stage classification (Awake, N1, N2, N3 and REM). The level of agreement using these descriptors was 74% as quantified by the Cohen's kappa

Pre-processing and Feature Extraction Techniques for EEGBCI Applications- A Review of Recent Research

The electrical waveforms generated by brain named electroencephalogram (EEG) signals, require certain special processing for using them as part of applications. EEG signals need special pre-processing to enable brain computer interfaces (BCI) capture essential details of the signal and use them for specific applications, including deriving decisions. In this paper, we focus on some of the recent works reported in the area of EEG pre-processing. Further, we discuss some of the reported works related to feature extraction of EEG signals for application in drowsiness detection and development of assistive technologies for persons with special need.Keywords: EEG signals, signal pre-processing, feature extraction, electroencephalography

Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition

We present a data driven approach to classify ictal (epileptic seizure) and non-ictal EEG signals using the multivariate empirical mode decomposition (MEMD) algorithm. MEMD is a multivariate extension of empirical mode decomposition (EMD), which is an established method to perform the decomposition and time-frequency (T−F) analysis of non-stationary data sets. We select suitable feature sets based on the multiscale T−F representation of the EEG data via MEMD for the classification purposes. The classification is achieved using the artificial neural networks. The efficacy of the proposed method is verified on extensive publicly available EEG datasets

Analysis of observed chaotic data

Thesis (Master)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2004Includes bibliographical references (leaves: 86)Text in English; Abstract: Turkish and Englishxii, 89 leavesIn this thesis, analysis of observed chaotic data has been investigated. The purpose of analyzing time series is to make a classification between the signals observed from dynamical systems. The classifiers are the invariants related to the dynamics. The correlation dimension has been used as classifier which has been obtained after phase space reconstruction. Therefore, necessary methods to find the phase space parameters which are time delay and the embedding dimension have been offered. Since observed time series practically are contaminated by noise, the invariants of dynamical system can not be reached without noise reduction. The noise reduction has been performed by the new proposed singular value decomposition based rank estimation method.Another classification has been realized by analyzing time-frequency characteristics of the signals. The time-frequency distribution has been investigated by wavelet transform since it supplies flexible time-frequency window. Classification in wavelet domain has been performed by wavelet entropy which is expressed by the sum of relative wavelet energies specified in certain frequency bands. Another wavelet based classification has been done by using the wavelet ridges where the energy is relatively maximum in time-frequency domain. These new proposed analysis methods have been applied to electrical signals taken from healthy human brains and the results have been compared with other studies