In analyzing a signal, especially a non-stationary signal, it is often necessary the desired signal to be segmented into small epochs. Segmentation can be performed by splitting the signal at time instances where signal amplitude or frequency change. In this paper, the signal is initially decomposed into signals with different frequency bands using wavelet transform. Then, fractal dimension of ...

In the present investigation a new methodology for macrostructural EEG characterization based on automatic segmentation has been applied to sleep analysis. A nonparametric statistical approach for EEG segmentation was chosen, because it minimizes the need for a priori information about a signal. The method provides the detection of change-points i.e. boundaries between quasi-stationary EEG segm...

In many non-stationary signal processing applications such as electroencephalogram (EEG), it is better to divide the signal into smaller segments during which the signals are pseudo-stationary. Therefore, they can be considered stationary and analyzed separately. In this paper a new segmentation method based on discrete wavelet transform (DWT) and Hiaguchi’s fractal dimension (FD) is proposed. ...

ASSESSMENT OF FETAL HEART RATE VARIABILITY AND REACTIVITY DURING LABOUR – A NOVEL APPROACH Sofia Blad 1 ENTROPY OF THE NEONATAL EEG Nils Löfgen 3 DETECTION OF BURSTS IN THE EEG OF POST ASPHYCTIC NEWBORNS Johan Löfhede 5 THE TRANSCEPHALIC ELECTRICAL IMPEDANCE METHOD PRINCIPLES FOR BRAIN TISSUE STATE MONITORING Fernando Seoane 7 SPECTRAL FEATURES OF THE EEG ARE UNLIKELY TO DIFFERENTIATE BETWEEN N...

In numerous signal processing applications, non-stationary signals should be segmented to piece-wise stationary epochs before being further analyzed. In this article, an enhanced segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals, such as electroencephalogram (EEG), magnetoencephalogram (MEG) and electromyogram (EMG), is proposed. In...

—The generalization and robustness of an electroencephalogram (EEG)-based computer aided diagnostic system are crucial requirements in actual clinical practice. To reach these goals, we propose a new EEG representation that provides a more realistic view of brain functionality by applying multi-instance (MI) framework to consider the non-stationarity of the EEG signal. The non-stationary charac...

This article presents a general methodology for processing non-stationary signals for the purpose of classification and localization. The methodology combines methods adapted from three complementary areas: time-frequency signal analysis, multichannel signal analysis and image processing. The latter three combine in a new methodology referred to as multichannel time-frequency image processing w...

Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recogni...

The record of human brain neural activities, namely electroencephalogram (EEG), is known to be nonstationary in general. In addition, the human head is a non-linear medium for such signals. In many applications, it is useful to divide the EEGs into segments in which the signals can be considered stationary. Here, Hilbert-Huang Transform (HHT), as an effective tool in signal processing is applie...