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An accurate sleep staging is crucial for the treatment of sleep disorders. Recently some studies demonstrated that the long range correlations of many physiological signals measured during sleep show some variations during the different sleep stages. In this study, detrended fluctuation analysis (DFA) is used to study the electroencephalogram (EEG) signal autocorrelation during different sleep stages. A classification of these stages is then made by introducing the calculated DFA power law exponents to a K-Nearest Neighbor classifier. The authors’ study reveals that a 2-D feature space composed of the DFA power law exponents of both the filtered THETA and BETA brain waves resulted in a classification accuracy of 93.52%, 93.52%, and 92.59% for the wake, non-rapid eye movement and rapid eye movement stages, respectively. The overall accuracy of the proposed system is 93.21%. The authors conclude that it might be possible to build an automated sleep assessment system based on DFA analysis of the sleep EEG signal.

Most of sleep disorders are diagnosed based on the sleep scoring and assessments. The purpose of this study is to combine detrended fluctuation analysis features and spectral features of single electroencephalograph (EEG) channel for the purpose of building an automated sleep staging system based on the hybrid prediction engine model. The testing results of the model were promising as the classification accuracies were 98.85%, 92.26%, 94.4%, 95.16% and 93.68% for the wake, non-rapid eye movement S1, non-rapid eye movement S2, non-rapid eye movement S3 and rapid eye movement sleep stages, respectively. The overall classification accuracy was 85.18%. We concluded that it might be possible to employ this approach to build an industrial sleep assessment system that reduced the number of channels that affected the sleep quality and the effort excreted by sleep specialists through the process of the sleep scoring.A. F. Farag et al. 585standardized method for characterizing normal sleep was published in 1968 by Allan Rechtschaffen and Anthony Kales [1]. Since then, this method has been considered the golden standard for sleep assessment.In 2009, the American Academy of Sleep Medicine (AASM) set the AASM manual for the scoring of sleep and associated events [2]. Sleep scoring classifies sleep into stages that correspond to certain brain activities. According to the AASM standard, sleep is divided into 5 stages, the awake stage (WK), the rapid eye movement (REM), and three non-rapid eye movements (NREM) sleep sub-stages (NREMS1, NREMS2, and NREMS3) that describe the depth of sleep.Both R&K and AASM manuals were originally developed to facilitate manual sleep scoring, not to be used in automated sleep scoring systems. Sleep assessment specialists exert considerable effort and time in the scoring of a single subject record. These manuals provide a subjective method for sleep scoring which may lead to inconsistent results. In a study that involved eight European sleep laboratories, the overall level of agreement in the scoring of the five sleep stages was only 76.8% [3].In the past few decades, many studies aimed to develop automated sleep scoring systems. Various automated systems differ in the extracted features, classification engines, or the bio-signals that these systems are based on. Spectral analysis features have the longest tradition in the analysis of sleep bio-signal due to the capability to quantify the different frequency contents of the signal similar to visual analysis [4]. The spectral features of sleep bio-signals could be calculated using FFT [5]-[7] and autoregressive models [8] [9].Many studies in the last decade switched from the conventional methods of spectral analysis to time-frequency analysis, and particularly using the Wavelets Transform [10]-[12]. Other feature extraction techniques include relative power band [8], Harmonic parameter (Hjorth parameter) [8] [13], K-means clustering based features [14] and detrended fluctuation analysis (DFA) of the raw EEG signals [15] [16].Detrended fluctuation an...

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We introduce using Sparse MRI with random radial trajectories in order to allow MRI images reconstruction from a small number of acquired k-space data, so fulfilling the demands of dynamic MRI. Different random radial trajectories are generated by varying the parameters of the added random perturbations to the radial trajectories. Images reconstruction is performed using the non-linear L1 norm reconstruction. Entropy is computed for the reconstructed images, as a quantitative measure for the reconstructed image quality. Both phantom simulation and real cardiac images are used in our experiments. Our results show that more sparsely sampled images can be reconstructed with higher quality compared to using nonrandomly sampled radial k-space trajectories.

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