Pre-Ictal Phase Detection with SVMs

Over 50 million persons worldwide are affected by epilepsy. Epilepsy is a brain disorder known for sudden, unexpected transitions from normal to pathological behavioral states called epileptic seizures. Epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. There is a need for a quick screening process that could help neurologist diagnose and determine the patient’s treatment. Electroencephalogram has been traditionally used to diagnose patients by evaluating those brain functions that may correspond to epilepsy. The objective of this paper is to implement a novel detection technique of pre-ictal state that announces epileptic seizures from the online EEG data analysis. Unlike most published methods, that are aimed to distinguish only the normal from the epilepsy state, in this work the pre-ictal state is introduced as a new patient status, thus differentiating three possible states: normal (healthy), pre-ictal and epileptic seizure. In this manner, the patient should get timely alert about the possible seizure attack so that she/he can stop with its activities and take safety precautions.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This work is partially supported by the Ministry of Education and Science of Spain under contract TIN2010-16144 and Junta de Andalucía under contract TIC-1692

ANALIZA WPŁYWU DOBORU ODPROWADZEŃ REFERENCYJNYCH ZAPISU EEG NA UZYSKANE WIDMO

The article presents the analysis of various kinds of montage applied in the analysis of electroencephalographic data. The present study covers analysis of data obtained as a result of EEG examination of several persons with the age of 23–24 years. Every person has undergone an examination with appliance of 21 electrodes in accordance with classic EEG record method applying the 10–20 system. The examined persons were asked to perform the following activities: sitting steadily with opened eyes, sitting steadily with closed eyes as well as typical cognitive activity i.e. silent reading of given text. Every fragment has been subjected to initial data analysis (filtering, artifact correction) and used for spectral analysis afterwards. The whole analysis has been realized on the basis of four EEG montage examples (two bipolar and two monopolar). For each of them comparative analysis concerning the following points has been carried out: EEG spectra as well as other measures, such as activity maps, diffraction histograms for separate waves and spectrum charts for the selected electrodes.Artykuł przedstawia analizę zastosowania różnego rodzaju montaży w analizie danych elektroencefalograficznych (EEG).Zaprezentowane studium przypadku obejmuje analizę danych z badania EEG kilku osób w wieku 23–24 lat. Każda osoba poddana została badaniu z wykorzystaniem 21 elektrod w klasycznym zapisie EEG z wykorzystaniem systemu 10–20. Osoby badane poproszone zostały o następujące aktywności: spokojne siedzenie z oczami otwartymi, zamkniętymi a także typową aktywność poznawczą – czytanie zadanego tekstu w myślach. Każdy fragment został poddany wstępnej analizie danych (filtracja, korekcja artefaktów) a następnie wykorzystany do analizy widmowej. Cała analiza została zrealizowana w oparciu o cztery przykładowe montaże EEG (dwa bipolarne i dwa monopolarne). Dla każdego z nich zrealizowano analizę porównawczą w odniesieniu do widm EEG a także innych miar, takich jak mapy aktywności, histogramy rozkładu poszczególnych fal oraz wykresy widmowe dla wybranych elektrod

Detección automática de espigas epilépticas basada en la estimación y promediación de energías de las bandas del EEG

En este trabajo se presenta un detector de espigas epilépticas. El algoritmo estima el espectro de potencia de la señal y calcula la energía relativa de diferentes bandas del EEG. Cada banda de energía es posteriormente promediada para estimar la posición temporal de las crisis epilépticas. El detector fue probado en 21 registros EEG invasivos adquiridos por el Epilepsy Center of the University Hospital of Freiburg. En 196 segmentos analizados (87 con espigas epilépticas) se obtuvo una sensibilidad del 85.39%. El método propuesto es apropiado para detectar crisis epilépticas en registro de larga duración, como los adquiridos en estudios prequirúrgicos, dada su simplicidad en el cálculo del algoritmo. El mismo permite reducir el tiempo empleado por médicos especialistas en la inspección visual de los registros EEG de muchas horas de duración.Sociedad Argentina de Informática e Investigación Operativ

A Linear Predictive Coding Filtering Method for Time-resolved Morphology of EEG Activity

This paper introduces a new time-resolved spectral analysis method based on the Linear Prediction Coding (LPC) method that is particularly suited to the study of the dynamics of EEG (Electroencephalography) activity. The spectral dynamics of EEG signals can be challenging to analyse as they contain multiple frequency components and are often corrupted by noise. The LPC Filtering (LPCF) method described here processes the LPC poles to generate a series of reduced-order filter transform functions which can accurately estimate the dominant frequencies. The LPCF method is a parameterized time-frequency method that is suitable for identifying the dominant frequencies of multiple-component signals (e.g. EEG signals). We define bias and the frequency resolution metrics to assess the ability of the LPCF method to estimate the frequencies. The experimental results show that the LPCF can reduce the bias of the LPC estimates in the low and high frequency bands and improved frequency resolution. Furthermore, the LPCF method is less sensitive to the filter order and has a higher tolerance of noise compared to the LPC method. Finally, we apply the LPCF method to a real EEG signal where it can identify the dominant frequency in each frequency band and significantly reduce the redundant estimates of the LPC method

Facilitating Joint Chaos and Fractal Analysis of Biosignals through Nonlinear Adaptive Filtering

Background: Chaos and random fractal theories are among the most important for fully characterizing nonlinear dynamics of complicated multiscale biosignals. Chaos analysis requires that signals be relatively noise-free and stationary, while fractal analysis demands signals to be non-rhythmic and scale-free. Methodology/Principal Findings: To facilitate joint chaos and fractal analysis of biosignals, we present an adaptive algorithm, which: (1) can readily remove nonstationarities from the signal, (2) can more effectively reduce noise in the signals than linear filters, wavelet denoising, and chaos-based noise reduction techniques; (3) can readily decompose a multiscale biosignal into a series of intrinsically bandlimited functions; and (4) offers a new formulation of fractal and multifractal analysis that is better than existing methods when a biosignal contains a strong oscillatory component. Conclusions: The presented approach is a valuable, versatile tool for the analysis of various types of biological signals. Its effectiveness is demonstrated by offering new important insights into brainwave dynamics and the very high accuracy in automatically detecting epileptic seizures from EEG signals

In-Network Data Reduction Approach Based On Smart Sensing

The rapid advances in wireless communication and sensor technologies facilitate the development of viable mobile-Health applications that boost opportunity for ubiquitous real- time healthcare monitoring without constraining patients' activities. However, remote healthcare monitoring requires continuous sensing for different analog signals which results in generating large volumes of data that needs to be processed, recorded, and transmitted. Thus, developing efficient in-network data reduction techniques is substantial in such applications. In this paper, we propose an in-network approach for data reduction, which is based on fuzzy formal concept analysis. The goal is to reduce the amount of data that is transmitted, by keeping the minimal-representative data for each class of patients. Using such an approach, the sender can effectively reconfigure its transmission settings by varying the target precision level while maintaining the required application classification accuracy. Our results show the excellent performance of the proposed scheme in terms of data reduction gain and classification accuracy, and the advantages that it exhibits with respect to state-of-the-art techniques.Scopu

A Lightweight Deep Learning Model for The Early Detection of Epilepsy

Epilepsy is a neurological disorder and non communicable disease which affects patient's health, During this seizure occurrence normal brain function activity will be interrupted. It may happen anywhere and anytime so it leads to very dangerous problems like sudden unexpected death. Worldwide seizure affected people are around 65% million. So it must be considered as serious problem for the early prediction.  A number of different types of screening tests will be conducted to assess the severity of the symptoms such as EEG,MRI, ECG, and ECG. There are several reasons why EEG signals are used, including their affordability, portability, and ability to display. The proposed model used bench-marked CHB-MIT EEG datasets for the implementation of early prediction of epilepsy ensures its seriousness and leads to perfect diagnosis. Researchers proposed Various ML /DL methods to  try for the early prediction of epilepsy but still it has some challenges in terms of efficiency and precision Seizure detection techniques typically employ the use of convolutional neural networks (CNN) and a bidirectional short- and long-term memory (Bi-LSTM) model in the realm of deep learning. This method leverages the strengths of both models to effectively analyze electroencephalogram (EEG) data and detect seizure patterns. These light weight models have been found to be effective in automatically detecting seizures in deep learning techniques with an accuracy rate of up to 96.87%. Hence, this system has the potential to be utilized for categorizing other types of physiological signals too, but additional research is required to confirm this