Clinicians and researchers divide sleep periods into different stages to analyze the quality of sleep. Despite advances in machine learning, sleep-stage classification is still performed manually. The process tedious time-consuming, but its automation has not yet been achieved. Another problem low accuracy due inconsistencies between somnologists. In this paper, we propose a method classify usi...

OBJECTIVE Potential differences between coherence and phase synchronization analyses of human sleep electroencephalogram (EEG) are assessed and occurrences of phase vs. complete synchronization between EEG signals from different locations during different sleep stages are investigated. METHODS Linear spectral coherence, mean phase coherence (MPC) z-score and Pearson's correlation coefficient ...

In previous sleep studies, it has been demonstrated that Poincare plots of RR intervals, which provide a beat to beat dynamic measure of heart rate variability, have distinctive and characteristic patterns according to sleep stages. This study was designed to evaluate the temporal relationship between heart rate variability and sleep electroencephalographic activity (EEG) by using the Pearson's...

We describe in this paper a smart multisensor kernel, given the shape of a wriststrap and able to characterise a current physiological state among a number of ones resulting from a previous analysis, and to transmit those results using a radio link. The main advantage of this product is its adaptability. As examples, we describe two applications which are the remote monitoring of elderly people...

Low-frequency EEG was analyzed quantitatively during 2 nights in 40 females and 34 males aged 26 to 101 years. Analyses were based on Rechtschaffen and Kales NREM sleep stages, on absolute low-frequency amplitude (i.e. power in the range of 0.2-2.0 Hz) and on low-frequency continuity. The latter parameter describes how much (0-100%) of the current slow-wave activity is continued in the near-fut...

Each stage of sleep can affect human health, and not getting enough at any may lead to disorder like parasomnia, apnea, insomnia, etc. Sleep-related diseases could be diagnosed using Convolutional Neural Network Classifier. However, this classifier has been successfully implemented into classification systems due high complexity low accuracy classification. The aim research is increase the redu...

In this paper we propose a new classification method for automatic sleep scoring using an artificial neural network based decision tree. It attempts to treat sleep scoring progress as a series of two-class problems and solves them with a decision tree made up of a group of neural network classifiers, each of which uses a special feature set and is aimed at only one specific sleep stage in order...

OBJECTIVE We report a series of seven consecutive cases of catathrenia (sleep related groaning) that differ from limited previous reports in the literature with regard to sleep stage and response to treatment. BACKGROUND Catathrenia was recently defined as a parasomnia in the International Classification of Sleep Disorders Diagnostic and Coding Manual (ICSD-2), but there is debate about its c...

Sleep plays a vital role in optimum working of the brain and body. Numerous people suffer from sleep-oriented illnesses like apnea, insomnia, etc. stage classification is primary process quantitative examination polysomnographic recording. scoring mainly based on experts’ knowledge which laborious time consuming. Hence, it can be essential to design automated sleep model using machine learning ...