Study of the Term Neonatal Brain Injury with combined Diffuse Optical Tomography and Electroencephalography

This thesis describes the application of combined diffuse optical tomography (DOT) and electroencephalography (EEG) in the investigation of neonatal term brain injury. With hypoxic ischaemic encephalopathy (HIE) and perinatal stroke being the most frequent contributors to brain injury in the term neonatal population, the first part of the thesis focuses on the description and ongoing requirement for their further investigation. In continuation to that, the characteristics and unique properties of both DOT and EEG are described and explored. The combination of these two modalities was utilised in elucidating the relationship between neuronal activity and cerebral haemodynamics both in physiological processes as well as in disease, by the infant’s cot side. This work differs to previous studies using near-infrared technologies and EEG, as a denser whole head array was used, offering the potential of 3-dimensional image reconstruction of the cortical haemodynamic events in relation to electro-cortical activity. These methods were applied in the study of critically ill infants presenting with seizures in the first few days of life. The relevant results are presented in three separate chapters of the thesis. Distinct neurophysiological phenomena such as seizures and burst suppression were detected and studied in association to underlying HIE. On the grounds of a pre-existing pilot study of our research group, distinct prolonged de-oxygenated cortical areas were identified following electrical seizure activity. Further exploration of infants with seizures provided limited supporting evidence. The investigation of burst suppression in HIE led to the first ever identification of repeated, waveform, cortical haemodynamic events in response to bursts of electrical activity with some spatial correlation to regions of brain injury. Further analysis of the low frequencies within the diffuse optical signal in cases of perinatal stroke, showed a consistent interhemispheric difference between the healthy and stroke-affected brain regions. The limitations, prospects and conclusions are presented in the final chapter. The use of simultaneous DOT and EEG offers a unique neuro-monitoring and neuro-investigating tool in the neonatal intensive care environment, which is safe, portable, and cost-effective, Ongoing research is required for the exploration and development of the methodology and its potential diagnostic properties

Principles of time-frequency feature extraction for change detection in non-stationary signals: applications to newborn EEG abnormality detection

This paper considers the general problem of detecting change in non-stationary signals using features observed in the time-frequency (t,f) domain, obtained using a class of quadratic time-frequency distributions (QTFDs). The focus of this study is to propose a methodology to define new (t,f) features by extending time-only and frequency-only features to the joint (t,f) domain for detecting changes in non-stationary signals. The (t,f) features are used as a representative subset characterizing the status of the observed non-stationary signal. Change in the signal is then reflected as a change in the (t,f) features. This (t,f) approach is applied to the problem of detecting abnormal brain activity in newborns (e.g. seizure) using measurements of the EEG for diagnosis and prognosis. In addition, a pre-processing stage for detecting artifacts in EEG signals for signal enhancement is studied and implemented separately. Overall results indicate that, in general, the (t,f) approach results in an improved performance in detecting artifacts and seizures in newborn EEG signals as compared to time-only or frequency-only features

Automatic Seizure Detection in Rats Using Laplacian EEG and Verification with Human Seizure Signals

Automated detection of seizures is still a challenging problem. This study presents an approach to detect seizure segments in Laplacian electroencephalography (tEEG) recorded from rats using the tripolar concentric ring electrode (TCRE) configuration. Three features, namely, median absolute deviation, approximate entropy, and maximum singular value were calculated and used as inputs into two different classifiers: support vector machines and adaptive boosting. The relative performance of the extracted features on TCRE tEEG was examined. Results are obtained with an overall accuracy between 84.81 and 96.51%. In addition to using TCRE tEEG data, the seizure detection algorithm was also applied to the recorded EEG signals from Andrzejak et al. database to show the efficiency of the proposed method for seizure detection

Time-frequency features for impedance cardiography signals during anesthesia using different distribution kernels

Objective: This works investigates the time-frequency content of impedance cardiography signals during a propofol-remifentanil anesthesia. Materials and Methods: In the last years, impedance cardiography (ICG) is a technique which has gained much attention. However, ICG signals need further investigation. Time-Frequency Distributions (TFDs) with 5 different kernels are used in order to analyze impedance cardiography signals (ICG) before the start of the anesthesia and after the loss of consciousness. In total, ICG signals from one hundred and thirty-one consecutive patients undergoing major surgery under general anesthesia were analyzed. Several features were extracted from the calculated TFDs in order to characterize the time-frequency content of the ICG signals. Differences between those features before and after the loss of consciousness were studied. Results: The Extended Modified Beta Distribution (EMBD) was the kernel for which most features shows statistically significant changes between before and after the loss of consciousness. Among all analyzed features, those based on entropy showed a sensibility, specificity and area under the curve of the receiver operating characteristic above 60%. Conclusion: The anesthetic state of the patient is reflected on linear and non-linear features extracted from the TFDs of the ICG signals. Especially, the EMBD is a suitable kernel for the analysis of ICG signals and offers a great range of features which change according to the patient’s anesthesia state in a statistically significant way.Peer ReviewedPostprint (author's final draft

New feature extraction approach for epileptic EEG signal detection using time-frequency distributions

10 pages, 6 figures.-- PMID: 20217264.This paper describes a new method to identify seizures in electroencephalogram (EEG) signals using feature extraction in time–frequency distributions (TFDs). Particularly, the method extracts features from the Smoothed Pseudo Wigner-Ville distribution using tracks estimated from the McAulay-Quatieri sinusoidal model. The proposed features are the length, frequency, and energy of the principal track. We evaluate the proposed scheme using several datasets and we compute sensitivity, specificity, F-score, receiver operating characteristics (ROC) curve, and percentile bootstrap confidence to conclude that the proposed scheme generalizes well and is a suitable approach for automatic seizure detection at a moderate cost, also opening the possibility of formulating new criteria to detect, classify or analyze abnormal EEGs.This work has been funded by the Spain CICYT grant TEC2008-02473.Publicad

Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients

This is the accepted manuscript version of the following article: Iosif Mporas, “Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients”, Expert Systems with Applications, Vol. 42(6), December 2014. The final published version is available at: http://www.sciencedirect.com/science/article/pii/S0957417414007763?via%3Dihub © 2014 Elsevier Ltd. All rights reserved.In this paper a seizure detector using EEG and ECG signals, as a module of a healthcare system, is presented. Specifically, the module is based on short-time analysis with time-domain and frequency-domain features and classification using support vector machines. The seizure detection module was evaluated on three subjects with diagnosed idiopathic generalized epilepsy manifested with absences. The achieved seizure detection accuracy was approximately 90% for all evaluated subjects. Feature ranking investigation and evaluation of the seizure detection module using subsets of features showed that the feature vector composed of approximately the 65%-best ranked parameters provides a good trade-off between computational demands and accuracy. This configurable architecture allows the seizure detection module to operate as part of a healthcare system in offline mode as well as in online mode, where real-time performance is needed.Peer reviewe