Temporal complexity of EEG encodes human alertness.

. Automatic human alertness monitoring has recently become an important research topic with important applications in many areas such as the detection of drivers' fatigue, monitoring of monotonous tasks that require a high level of alertness such as traffic control and nuclear power plant monitoring, and sleep staging. In this study, we propose that balanced dynamics of Electroencephalography (EEG) (so called EEG temporal complexity) is a potentially useful feature for identifying human alertness states.

Neonatal EEG seizure detection using a new signal structural complexity measure based on matching pursuit decomposition with nonstationary dictionary.

Background And Objective: In newborns, it is often difficult to accurately differentiate between seizure and non-seizure based solely on clinical manifestations. This highlights the importance of electroencephalogram (EEG) in the recognition and management of neonatal seizures. This paper proposes an effective algorithm for the detection of neonatal seizure using multichannel EEG.

Automatic fetal movement recognition from multi-channel accelerometry data.

Background And Objective: Significant health care resources are allocated to monitoring high risk pregnancies to minimize growth compromise, reduce morbidity and prevent stillbirth. Fetal movement has been recognized as an important indicator of fetal health. Studies have shown that 25% of pregnancies with decreased fetal movement in the third trimester led to poor outcomes at birth.

Range Entropy: A Bridge between Signal Complexity and Self-Similarity.

Approximate entropy () and sample entropy () are widely used for temporal complexity analysis of real-world phenomena. However, their relationship with the Hurst exponent as a measure of self-similarity is not widely studied. Additionally, and are susceptible to signal amplitude changes.

Ventricular ectopic beats classification using Sparse Representation and Gini Index.

In this study, we consider using sparse representation and the Gini Index (GI) for Arrhythmia classification. Our approach involves, first, designing a separate dictionary for each Arrhythmia class using a set of labeled training QRS complexes. Sparse representations, based on the designed dictionaries, of each new test QRS complex are then calculated.

A Transform-Based Feature Extraction Approach for Motor Imagery Tasks Classification.

In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain-computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix.

Neonatal EEG at scalp is focal and implies high skull conductivity in realistic neonatal head models.

The potential improvements in spatial resolution of neonatal EEG used in source localization have been challenged by the insufficiencies in realistic neonatal head models. Our present study aimed at using empirical methods to indirectly estimate skull conductivity; the model parameter that is known to significantly affect the behavior of newborn scalp EEG and cause it to be markedly different from that of an adult. To this end, we used 64 channel EEG recordings to study the spatial specificity of scalp EEG by assessing the spatial decays in focal transients using both amplitudes and between-c'hannels linear correlations.

Newborn seizure detection based on heart rate variability.

In this paper, we investigate the use of heart rate variability (HRV) for automatic newborn seizure detection. The proposed method consists of a sequence of processing steps, namely, obtaining HRV from the ECG, extracting a discriminating HRV feature set, selecting an optimal subset from the full feature set, and, finally, classifying the HRV into seizure/nonseizure using a supervised statistical classifier. Due to the fact that HRV signals are nonstationary, a set of time-frequency features from the newborn HRV is proposed and extracted.

Robust time-frequency analysis of newborn EEG seizure corrupted by impulsive artefacts.

The newborn EEG seizure is a nonstationary signal. The time-varying nature of the newborn EEG seizure can be characterized by time-frequency representations (TFRs) such as quadratic time-frequency distributions. The underlying time-frequency signatures of newborn EEG seizure, however, can be severely masked by short-time and high amplitude (STHA), or impulsive, artefacts.

A nonstationary model of newborn EEG.

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques.

Time-frequency analysis of heart rate variability for neonatal seizure detection.

The ECG has been much neglected in automatic seizure detection in the newborn. Changes in heart rate and ECG rhythm are often found in animal and adult patients with seizure. However, little is known about heart rate variability (HRV) changes in human neonate during seizure.

Time-frequency based newborn EEG seizure detection using low and high frequency signatures.

The nonstationary and multicomponent nature of newborn EEG seizures tend to increase the complexity of the seizure detection problem. In dealing with this type of problem, time-frequency based techniques were shown to outperform classical techniques. Neonatal EEG seizures have signatures in both low frequency (lower than 10 Hz) and high frequency (higher than 70 Hz) areas.