Jitka Mohylová

Neonatal EEG sleep stages modelling by temporal profiles

The paper deals with the application of the EEG temporal profiles for the neonatal sleep stages modelling. The temporal profiles created by adaptive segmentation and cluster analysis reflect the time structure of the EEG during different periods of sleep. They can be used for neonatal EEG quantification and for the detection of sleep stage changes.

Pattern recognition of epileptic EEG graphoelements with adaptive segmentation, supervised and unsupervised learning algorithms

The electroencephalogram (EEG) provides markers of brain disturbances in the field of epilepsy. In short duration EEG data recordings, the epileptic graphoelements may not manifest. The visual analysis of lengthy signals is a tedious task. It is necessary to track the EEG activity on the computer screen and to detect the epileptiform graphoelements and the other pathological activity. The automation of the process is needed. We will compare the EEG wave classification both by supervised and unsupervised learning algorithms. The feasibility to detect the changes in the microstructure of epileptic activity will be verified.

Application of Artificial Neural Networks for Analyses of EEG Record with Semi-Automated Etalons Extraction: A Pilot Study

Application of artificial neural network (ANN) classification – multilayer perceptron (MLP) with simulated annealing for initialization and genetic algorithm for weight optimization on multi-channel EEG record is presented here. The novelty of the approach lies in the semi-automated etalon extraction. The etalons are suggested by the k-means algorithm and verified/edited by an expert. The whole process of EEG record consists of multichannel adaptive segmentation, feature extraction from segments, semi-automatic process of etalons extraction by the k-means cluster analysis leading to color segment identification and continuing with manual choice of segments for etalons by the expert and feature extraction of chosen etalons. Subsequent classification by ANN leads to unique color identification of segments in the EEG record and additionally in temporal profile. Our goal is to help the physician by mimetic software because the examination of long multichannel EEG is a tedious work.

Estimation of the Time Delay of Epileptic Spikes by ICA

The contribution concentrates on application of Independent Component Analysis (ICA) for the detection of small time delays of epileptic spikes in electroencephalographic (EEG) recordings. The ICA method isolates spikes activity by decomposing the input EEG record into independent components. Some of them contain epileptic spikes. ICA detects the time delay of epileptic spikes between channels by separating the epileptic spikes into two or more components. We propose a method of epileptic focus location by ICA from EEG recordings which contain epileptic spikes. The analysis allows presentation of the results in the form of topographic maps. The method was tested on real EEG background signal with artificially simulated epileptic spikes and on EEG records containing real epileptic activity, obtained in four epileptic patients. The tests were used for a comparison with the results of a visual analysis. The tests confirmed a satisfactory agreement between computerized and visual assessments.

Comparative effectiveness of ICA and PCA in extraction of fetal ECG from abdominal signals: Toward non-invasive fetal monitoring

Non-adaptive signal processing methods have been successfully applied to extract fetal electrocardiograms (fECGs) from maternal abdominal electrocardiograms (aECGs); and initial tests to evaluate the efficacy of these methods have been carried out by using synthetic data. Nevertheless, performance evaluation of such methods using real data is a much more challenging task and has neither been fully undertaken nor reported in the literature. Therefore, in this investigation, we aimed to compare the effectiveness of two popular non-adaptive methods (the ICA and PCA) to explore the non-invasive (NI) extraction (separation) of fECGs, also known as NI-fECGs from aECGs. The performance of these well-known methods was enhanced by an adaptive algorithm, compensating amplitude difference and time shift between the estimated components. We used real signals compiled in 12 recordings (real01–real12). Five of the recordings were from the publicly available database (PhysioNet-Abdominal and Direct Fetal Electrocardiogram Database), which included data recorded by multiple abdominal electrodes. Seven more recordings were acquired by measurements performed at the Institute of Medical Technology and Equipment, Zabrze, Poland. Therefore, in total we used 60 min of data (i.e., around 88,000 R waves) for our experiments. This dataset covers different gestational ages, fetal positions, fetal positions, maternal body mass indices (BMI), etc. Such a unique heterogeneous dataset of sufficient length combining continuous Fetal Scalp Electrode (FSE) acquired and abdominal ECG recordings allows for robust testing of the applied ICA and PCA methods. The performance of these signal separation methods was then comprehensively evaluated by comparing the fetal Heart Rate (fHR) values determined from the extracted fECGs with those calculated from the fECG signals recorded directly by means of a reference FSE. Additionally, we tested the possibility of non-invasive ST analysis (NI-STAN) by determining the T/QRS ratio. Our results demonstrated that even though these advanced signal processing methods are suitable for the non-invasive estimation and monitoring of the fHR information from maternal aECG signals, their utility for further morphological analysis of the extracted fECG signals remains questionable and warrants further work.

Band Stop Filter with a Synthetic Inductor with Series Resistance and a Real Operational Amplifier

This paper deals with application of simple synthetic inductor with series resistance (non-ideal gyrator). Those inductors are seldom used. However, if we use this inductor in the arm of bridge, the circuit is able to achieve high performance. In this way we can get band stop filter with good performance. In the paper we solve influence of real OPA properties. Theoretical considerations are verified by means of simulations (MicroCap) and experiments. Based on theoretical considerations, simulations and experiments are finally determined by criteria that must meet real operational amplifier to make the circuit performed well.

Modelling of Stray Currents Near a Railway Platform

The article describes simulations of the occurrence of stray currents in the vicinity of a SUDOP-type railway platform. Stray currents occur in the vicinity of a railway platform during the passage of a railway vehicle. In a fault-free state, all metallic parts are separated from the rail by means of a surge arrester. In the case of a breakdown of the surge arrester, the full voltage which is on the rail at the time of the passing railway vehicle may also pass onto the metallic parts. The surge arresters are checked every 6 months during maintenance work on the overhead lines according to Annex No. 1 of SZDC internal regulation E 500. The article, therefore, carries out a simulation of stray currents in exactly this type of breakdown. In addition, to the model, we included the influence of the outdoor lighting cable which is located in the ground under the platform. For the purposes of solving the model, we are using a commercially available program which solves partial differential equations and uses the finite element method for calculation.

Fuzzy c-Means Algorithm in Automatic Classification of EEG

The electroencephalogram (EEG) provides markers of brain disturbances in the field of epilepsy. In short duration EEG data recordings, the epileptic graphoelements may not manifest. The visual analysis of lengthy signals is a tedious task. It is necessary to track the activity on the computer screen and to detect the epileptiform graphoelements and the other pathological activity. The automation of the process is suggested. The procedure is based on processing temporal profiles computed by means of multichannel adaptive segmentation and subsequent classification of detected signal graphoelements. The temporal profiles, function of the class membership in the course of time, reflect the dynamic EEG microstructure and may be used for visual indication of abnormal changes in the EEG using different colors. We will show that Fuzzy c-means (FCM) algorithm can be used for correct classification of epileptic pattern, creating homogeneous compact classes of significant EEG segments.

An EEG Classification Approach Based on Intrinsic Signal Properties and Wavelets

In both medicine and technology, human brainwave recognition is a very much discussed topic because of its wide applications. These range from disease diagnostic to brain-computer interfaces used to control various devices. In this paper, we present a novel combined approach to epileptic spike recognition from recorded electroencephalograms. The highest accuracy obtained is 99.22 % when measured with the 10-fold cross-validation of a single patient’s data. This is achieved by combining a continuous wavelet transform to provide classification marks and classifiers to assign them. Results are also collected for the scenario of training the classifiers on one record of the patient and testing on another, taken at a different time. This provides an insight on the classifier’s ability to generalize for a given patient.

47. Automatic classification of EEG graphoelements (workshop)

Outline 1. Motivation, why and which types of EEG graphoelements to classify automatically. 2. Discriminative features extraction. a. Multichannel adaptive segmentation of non-stationary signals. b. Heuristic features extraction based on physician’s point of view. c. Extraction, selection, reduction and features standardization. d. Application of PCA – Principal Component Analysis and ICA- Independent Component Analysis (artefacts rejection). 3. Supervised and non-supervised learning classical and fuzzy. a. Statistical pattern recognition, k-NN, k-means. b. Artificial neural networks, multilayer perceptron. c. Fuzzy sets for improving the homogeneity classes of EEG segments (fuzzy c-means, fuzzy k-NN). 4. Semi-automatic extraction of prototypes from original EEG recordings, pre-processing by cluster analysis in the learning phase (prototypes gathering), involving of expert into the process of etalons extraction. 5. Graphic visualization of results. a. Color identification of significant graphoelements. b. Temporal profiles – graphs of segments membership in EEG classes in the course of time. c. Automated processing of neonatal temporal profiles (sleep stages detection). d. Statistical diagrams of percentual EEG graphoelements occurrence. 6. Applications, case studies. a. Automatic detection and classification of epileptic graphoelements for long-term EEG monitoring. b. Automatic detection of sleep stage changes in neonatal EEG, statistics of quiet and active sleep. c. Practical examples of etalons extraction.