Comparative study of nonlinear properties of EEG signals of a normal person and an epileptic patient

Background: Investigation of the functioning of the brain in living systems has been a major effort amongst scientists and medical practitioners. Amongst the various disorder of the brain, epilepsy has drawn the most attention because this disorder can affect the quality of life of a person. In this paper we have reinvestigated the EEGs for normal and epileptic patients using surrogate analysis, probability distribution function and Hurst exponent. Results: Using random shuffled surrogate analysis, we have obtained some of the nonlinear features that was obtained by Andrzejak \textit{et al.} [Phys Rev E 2001, 64:061907], for the epileptic patients during seizure. Probability distribution function shows that the activity of an epileptic brain is nongaussian in nature. Hurst exponent has been shown to be useful to characterize a normal and an epileptic brain and it shows that the epileptic brain is long term anticorrelated whereas, the normal brain is more or less stochastic. Among all the techniques, used here, Hurst exponent is found very useful for characterization different cases. Conclusions: In this article, differences in characteristics for normal subjects with eyes open and closed, epileptic subjects during seizure and seizure free intervals have been shown mainly using Hurst exponent. The H shows that the brain activity of a normal man is uncorrelated in nature whereas, epileptic brain activity shows long range anticorrelation.Comment: Keywords:EEG, epilepsy, Correlation dimension, Surrogate analysis, Hurst exponent. 9 page

Complexity Measures for Normal and Epileptic EEG Signals using ApEn, SampEn and SEN

There are numerous applications of EEG signal processing such as monitoring alertness, coma, and brain death, controlling an aesthesia, investigating epilepsy and locating seizure origin, testing epilepsy drug effects, monitoring the brain development, and investigating mental disorders; where data size is too long and requires long time to observe the data by clinician or neurologist. EEG signal processing techniques can be used effectively in such applications. The configuration of the signal waveform may contain valuable and useful information about the different state of the brain since biological signal is highly random in both time and frequency domain. Thus computerized analysis is necessary. Being a non-stationary signal, suitable analysis is essential for EEG to differentiate the normal EEG and epileptic seizures. The importance of entropy based features to recognize the normal EEGs, and ictal as well as interictal epileptic seizures. Three features, such as, Approximate entropy, Sample entropy, and Spectral entropy are used to take out the quantitative entropy features from the given EEG time series data of various time frames of 0.88s, and 1s .Average value of entropies for epileptic time series is less than non epileptic time series

Classification of Signals by Means of Genetic Programming

[Abstract] This paper describes a new technique for signal classification by means of Genetic Programming (GP). The novelty of this technique is that no prior knowledge of the signals is needed to extract the features. Instead of it, GP is able to extract the most relevant features needed for classification. This technique has been applied for the solution of a well-known problem: the classification of EEG signals in epileptic and healthy patients. In this problem, signals obtained from EEG recordings must be correctly classified into their corresponding class. The aim is to show that the technique described here, with the automatic extraction of features, can return better results than the classical techniques based on manual extraction of features. For this purpose, a final comparison between the results obtained with this technique and other results found in the literature with the same database can be found. This comparison shows how this technique can improve the ones found.Instituto de Salud Carlos III; RD07/0067/0005Xunta de Galicia; 10SIN105004P

Classification of Epileptic and Non-Epileptic Electroencephalogram (EEG) Signals Using Fractal Analysis and Support Vector Regression

Seizures are a common symptom of this neurological condition, which is caused by the discharge of brain nerve cells at an excessively fast rate. Chaos, nonlinearity, and other nonlinearities are common features of scalp and intracranial Electroencephalogram (EEG) data recorded in clinics. EEG signals that aren't immediately evident are challenging to categories because of their complexity. The Gradient Boost Decision Tree (GBDT) classifier was used to classify the majority of the EEG signal segments automatically. According to this study, the Hurst exponent, in combination with AFA, is an efficient way to identify epileptic signals. As with any fractal analysis approach, there are problems and factors to keep in mind, such as identifying whether or not linear scaling areas are present. These signals were classified as either epileptic or non-epileptic by using a combination of GBDT and a Support Vector Regression (SVR). The combined method's identification accuracy was 98.23%. This study sheds light on the effectiveness of AFA feature extraction and GBDT classifiers in EEG classification. The findings can be utilized to develop theoretical guidance for the clinical identification and prediction of epileptic EEG signals. Doi: 10.28991/ESJ-2022-06-01-011 Full Text: PD

Exploring strategies for classification of external stimuli using statistical features of the plant electrical response

This is the author accepted manuscript. The final version is available from the Royal Society via the DOI in this record.Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis-based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In the process, we also propose two standard features which consistently give good classification results for three types of stimuli--sodium chloride (NaCl), sulfuric acid (H₂SO₄) and ozone (O₃). This may facilitate reduction in the complexity involved in computing all the features for online classification of similar external stimuli in future.The work reported in this paper was supported by project PLants Employed As SEnsor Devices (PLEASED), EC grant agreement number 296582

Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks

The recording of seizures is of primary interest in the evaluation of epileptic patients. Seizure is the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behavior usually lasts from seconds to minutes. Since seizures, in general, occur infrequently and unpredictably, automatic detection of seizures during long-term electroencephalograph (EEG) recordings is highly recommended. As EEG signals are nonstationary, the conventional methods of frequency analysis are not successful for diagnostic purposes. This paper presents a method of analysis of EEG signals, which is based on time-frequency analysis. Initially, selected segments of the EEG signals are analyzed using time-frequency methods and several features are extracted for each segment, representing the energy distribution in the time-frequency plane. Then, those features are used as an input in an artificial neural network (ANN), which provides the final classification of the EEG segments concerning the existence of seizures or not. We used a publicly available dataset in order to evaluate our method and the evaluation results are very promising indicating overall accuracy from 97.72% to 100%

Use of the hurst exponent for analysis of electrocortical epileptiform activity induced in rats by administration of camphor essential oil or 1,8-cineole

In this study, we investigated the presence of long-range correlation effects in the electrocortical activity of rats using the Hurst exponent (H) calculated by dispersion analysis (DA) and an aggregated variance method (AGV). A slow decline of the autocorrelation function during time expansion and the existence of a correlation between distant time points of electrocorticograms (ECoGs) were shown to be typical of various pathophysiological states. In these cases, the H values were within a 0.5 < H < 1 range. A particularly slow decay of the autocorrelation function is typical of a long-range dependence (LRD). We found that ECoGs after i.p. administrations of camphor essential oil or its main constituent, 1,8-cineole, included attacks of uncontrolled electrical discharges and showed the presence of longrange correlation effects. Such findings are in agreement with recent data obtained by other authors and suggest that initiation of seizures can be predicted by certain ECoG indices. We estimated the critical period where the H values for ECoGs containing uncontrolled electrical discharges continuously increased within a few minutes before the attack. We believe that the AGV demonstrates certain advantages over DA in calculations of the H.Ми виявляли присутність ділянок тривалої кореляції в електрокортикограмах (ЕКоГ) щурів, використовуючи побудову експоненти Харста (Н). Останню розраховували на основі дисперсійного аналізу (DA) або методу згрупованих варіанс (AGV). В ЕКоГ, зареєстрованих у різних фізіологічних станах, спостерігалися повільне затухання аутокореляційної функції при розтягненні часової шкали та кореляція між віддаленими часовими точками. У цих випадках значення Н знаходилося в діапазоні 0.5<H<1.0. Особливо повільне затухання аутокореляційної функції свідчить про наявність довгодіапазонної залежності (LRD). Ми встановили, що в ЕКоГ, зареєстрованих після внутрішньоочеревинних ін’єкцій камфорної олії або її основного активного компонента 1,8-цинеолу, були наявні спалахи неконтрольованих розрядів (епілептиформні епізоди) та прояви тривалої кореляції. Ці спостереження узгоджуються з результатами, про котрі повідомляли інші автори, та вказують на можливість передбачати можливий розвиток судомної активності. Як виявилося, критичний період, у межах котрого величина Н для ЕКоГ з наявністю епілептиформних епізодів і котрий передує розвитку судомної активності, складає декілька хвилин. Ми вважаємо, що метод AGV при розрахунку Н має преференції порівняно з DА