Blood pressure and cardiovascular parameters during sleep arousals.

Sleep arousal is generally known as a transient episode of wakefulness into the sleepiness. Sleep arousals can be classified based on their association and accompany with pathological episodes. In this paper, our objective was to find out whether various types of sleep arousals influence on blood pressure and Heart Rate Variability (HRV).

Sleep spindle detection using multivariate Gaussian mixture models.

In this research study we have developed a clustering-based automatic sleep spindle detection method that was evaluated on two different databases. The databases consisted of 20 all-night polysomnograph recordings. Past detection methods have been based on subject-independent and some subject-dependent parameters, such as fixed or variable thresholds to identify spindles.

Pulse transit time and heart rate variability in sleep staging.

This paper presents a new and robust algorithm for detection of sleep stages by using the lead I of the Electrocardiography (ECG) and a fingertip Photoplethysmography (PPG) sensor, validated using multiple overnight PSG recordings consisting of 20 human subjects (9 insomniac and 11 healthy). Heart Rate Variability (HRV) and Pulse Transit Time (PTT) biomarkers which were extracted from ECG and PPG biosignals then employed to extract features. Distance Weighted k-Nearest Neighbours (DWk-NN) was used as classifier to differentiate sleep epochs.

Photoplethysmography derivatives and pulse transit time in overnight blood pressure monitoring.

Overnight continuous blood pressure measurement provides simultaneous monitoring of blood pressure and sleep architecture. By this means, we are able to investigate whether different sleep events are associated to blood pressure fluctuations. In this paper, we used the Pulse Transit Time (PTT) to develop and evaluate functions for measurement of blood pressure.

Insomnia Characterization: From Hypnogram to Graph Spectral Theory.

Objective: To quantify and differentiate control and insomnia sleep onset patterns through biomedical signal processing of overnight polysomnograms.

Methods: The approach consisted of three tandem modules: 1) biosignal processing module, which used state-space time-varying autoregressive moving average (TVARMA) processes with recursive particle filter, 2) hypnogram generation module that implemented a fuzzy inference system (FIS), and 3) insomnia characterization module that discriminated between control and subjects with insomnia using a logistic regression model trained with a set of similarity measures ( d1, d2 , d3, d4). The study employed sleep onset periods from 16 control and 16 subjects with insomnia.

Searching arousals: A fuzzy logic approach.

This paper presents a computational approach to detect spontaneous, chin tension and limb movement-related arousals by estimating neuronal and muscular activity. Features extraction is carried out by Time Varying Autoregressive Moving Average (TVARMA) models and recursive particle filtering. Classification is performed by a fuzzy inference system with rule-based decision scheme based upon the AASM scoring rules.

Automated sleep spindle detection using IIR filters and a Gaussian Mixture Model.

Sleep spindle detection using modern signal processing techniques such as the Short-Time Fourier Transform and Wavelet Analysis are common research methods. These methods are computationally intensive, especially when analysing data from overnight sleep recordings. The authors of this paper propose an alternative using pre-designed IIR filters and a multivariate Gaussian Mixture Model.

Characterising insomnia: A graph spectral theory approach.

This paper introduces a computational approach to characterise healthy controls and insomniacs based on graph spectral theory. Based upon expert-generated hypnograms of sleep onset periods, a network of sleep stages transitions is derived to compute four similarity distances amongst subjects' sleeping patterns. A subsequent statistical analysis is performed to differentiate the 16-subject healthy group from a 16-patient disordered cohort.

Sleep electroencephalography and heart rate variability interdependence amongst healthy subjects and insomnia/schizophrenia patients.

The quantification of interdependencies within autonomic nervous system has gained increasing importance to characterise healthy and psychiatric disordered subjects. The present work introduces a biosignal processing approach, suggesting a computational resource to estimate coherent or synchronised interactions as an eventual supportive aid in the diagnosis of primary insomnia and schizophrenia pathologies. By deploying linear, nonlinear and statistical methods upon 25 electroencephalographic and electrocardiographic overnight sleep recordings, the assessment of cross-correlation, wavelet coherence and [Formula: see text]:[Formula: see text] phase synchronisation is focused on tracking discerning features amongst the clinical cohorts.

Comparison between human awake, meditation and drowsiness EEG activities based on directed transfer function and MVDR coherence methods.

This study examined the electroencephalogram functional connectivity (coherence) and effective connectivity (flow of information) of selected brain regions during three different attentive states: awake, meditation and drowsiness. For the estimation of functional connectivity (coherence), Welch and minimum variance distortionless response (MVDR) methods were compared. The MVDR coherence was found to be more suitable since it is both data and frequency dependent and enables higher spectral resolution, while Welch's periodogram-based approach is both data and frequency independent.

Information flow and coherence of EEG during awake, meditation and drowsiness.

A comparison of coupling (information flow) and coherence (connectedness) of the brain regions between human awake, meditation and drowsiness states was carried out in this study. The Directed Transfer Function (DTF) method was used to estimate the coupling or brain's flow of information between different regions during each condition. Welch and Minimum Variance Distortionless Response (MVDR) methods were utilised to estimate the coherence between brain areas.

Automated Sleep Spindle detection using novel EEG features and mixture models.

Research in automated Sleep Spindle detection has been highly explored in the past few years. Although a number of automated techniques were developed, many of them were based on using fixed parameters or thresholds which do not consider subject specific differences. In this research study, we introduce a novel method of sleep spindle detection using Gaussian Mixture Models with no fixed parameters or thresholds.

Linear and non-linear interdependence of EEG and HRV frequency bands in human sleep.

The characterisation of functional interdependencies of the autonomic nervous system (ANS) stands an evergrowing interest to unveil electroencephalographic (EEG) and Heart Rate Variability (HRV) interactions. This paper presents a biosignal processing approach as a supportive computational resource in the estimation of sleep dynamics. The application of linear, non-linear methods and statistical tests upon 10 overnight polysomnographic (PSG) recordings, allowed the computation of wavelet coherence and phase locking values, in order to identify discerning features amongst the clinical healthy subjects.

Automatic detection of the wake and stage 1 sleep stages using the EEG sub-epoch approach.

Studies by Rechtschaffen and Kales (R&K), rely on 30-sec epochs to score sleep stages. In this paper, we introduce a new approach based on three consecutive and non-consecutive 6-sec sub-epochs for the detection of the wake stage and stage 1 sleep. The Relative Spectral Energy Band (RSEB) is used as a feature extraction from the electroencephalographic (EEG) signal.

A single-trial toolbox for advanced sleep polysomnographic preprocessing.

The application of polysomnographic (PSG) studies for monitoring sleep activity is a multi-parametric practice that involves a diverse group of biological signals. A suitable preprocessing of such signals assures a more profitable feature extraction and classification operations. Therefore, the proposed preprocessing toolbox performs segmentation, filtering, denoising, whitening and artefact removal tasks upon multi-channel PSG recordings.

Evaluation of FCS self and peer-assessment approach based on Cooperative and Engineering Design learning.

The Cooperative Learning in Engineering Design curriculum can be enhanced with structured and timely self and peer assessment teaching methodologies which can easily be applied to any Biomedical Engineering curriculum. A study was designed and implemented to evaluate the effectiveness of this structured and timely self and peer assessment on student team-based projects. In comparing the 'peer-blind' and 'face-to-face' Fair Contribution Scoring (FCS) methods, both had advantages and disadvantages.

The dark side of the alpha rhythm: fMRI evidence for induced alpha modulation during complete darkness.

The unique role of the EEG alpha rhythm in different states of cortical activity is still debated. The main theories regarding alpha function posit either sensory processing or attention allocation as the main processes governing its modulation. Closing and opening eyes, a well-known manipulation of the alpha rhythm, could be regarded as attention allocation from inward to outward focus though during light is also accompanied by visual change.

Cross-correlation of EEG frequency bands and heart rate variability for sleep apnoea classification.

Sleep apnoea is a sleep breathing disorder which causes changes in cardiac and neuronal activity and discontinuities in sleep pattern when observed via electrocardiogram (ECG) and electroencephalogram (EEG). Using both statistical analysis and Gaussian discriminative modelling approaches, this paper presents a pilot study of assessing the cross-correlation between EEG frequency bands and heart rate variability (HRV) in normal and sleep apnoea clinical patients. For the study we used EEG (delta, theta, alpha, sigma and beta) and HRV (LF(nu), HF(nu) and LF/HF) features from the spectral analysis.

Alterations in sleep EEG activity during the hypopnoea episodes.

The Obstructive Sleep Apnoea Hypopnoea Syndrome (OSAH) means "cessation of breath" during the sleep hours and the sufferers often experience related changes in the electrical activity of the brain and heart. The aim of this paper is to investigate any possible changes in the human electroencephalographic (EEG) activity due to hypopnoea (mild case of cessation of breath) occurrences by applying the non-linear and linear time series methods. The results from this study indicated significant changes in the human EEG activity due to hypopnoea episodes by applying the non-linear, Lyapunov exponent method at C3 EEG electrode site.

Correlation of sleep EEG frequency bands and Heart Rate Variability.

Sleep apnoea is a sleep breathing disorder which causes changes in cardiac and neuronal activity and discontinuities in sleep pattern when observed via electrocardiogram (ECG) and electroencephalogram (EEG). This paper presents a pilot study result of assessing the correlation between EEG frequency bands and ECG Heart Rate Variability (HRV) in normal and sleep apnoea human clinical patients at different sleep stages. In sleep apnoea patients, the results have shown that EEG delta, sigma and beta bands exhibited a strong correlation with cardiac HRV parameters at different sleep stages.

Alterations of human electroencephalographic activity caused by multiple extremely low frequency magnetic field exposures.

In the past, many studies have claimed that extremely low frequency (ELF) magnetic field (MF) exposures could alter the human electroencephalographic (EEG) activity. This study aims at extending our ELF pilot study to investigate whether MF exposures at ELF in series from 50, 16.66, 13, 10, 8.

EEG inter/intra-hemispheric coherence and asymmetric responses to visual stimulations.

This study has attempted to increase the meaning and significance of findings in the experimental areas of electroencephalographic (EEG) visual or photic driving. The aim of this study was to investigate whether the visual stimulation at particular extremely low frequency order could possibly induce changes in the corresponding EEG frequency bands by examining the functional connectedness between brain regions. This was evaluated by applying the improved experimental protocol and objective using non-parametric spectral estimation coherence algorithm.

The effect of GSM-like ELF radiation on the alpha band of the human resting EEG.

Mobile phone handsets such as those operating in the GSM network emit extremely low frequency electromagnetic fields ranging from DC to at least 40 kHz. As a subpart of an extended protocol, the influence of these fields on the human resting EEG has been investigated in a fully counter balanced, double blind, cross-over design study that recruited 72 healthy volunteers. A decrease in the alpha frequency band was observed during the 20 minutes of ELF exposure in the exposed hemisphere only.

Sleep onset estimator: evaluation of parameters.

The electroencephalographic (EEG) alterations during the human sleep onset (falling asleep period) has been evaluated by several studies in the past. However, the analysis part has been limited due to standard signal processing methods. This paper has attempted to evaluate a number of advanced parameters for improved sleep onset estimation, such as EEG non-parametric coherence, power frequency and spectral band power.

Alterations in sleep EEG activity during the hypopnoea episodes.

The Obstructive Sleep Apnoea Hypopnoea Syndrome (OSAH) means 'cessation of breath' during the sleep hours and the sufferers often experience related changes in the electrical activity of the brain and heart. The aim of this paper is to investigate any possible changes in the human electroencephalographic (EEG) activity due to hypopnoea (mild case of cessation of breath) occurrences by applying the non-linear and linear time series methods. The results from this one-subject study indicated significant changes in the human EEG activity due to hypopnoea episodes by applying the non-linear, Lyapunov exponent method at C3 EEG electrode site.