Effects of the CPAP Treatment on the NON-REM Sleep Microstructures in Patients with Severe Apnea-Hypoapnea Syndrome

Sleep quality is affected in patients with sleep apnea- hypopnea syndrome (SAHS) with nocturnal and diurnal consequences. Most of these patients who are treated with positive airway pressure (CPAP) return to normal sleep patterns. We could consider good sleepers those patients who present more sleep spindles in stage II, and slower wave sleep as a good sign of better sleep quality. The objective in this research study was to compare the microstructure of stage II using the number of spindles and the increase of slow wave sleep before and after CPAP night titration. We developed a wavelet filter using a spline cubic function from a wavelet mother, which was appropriate to be used over electroencephalographic signal. By means of this filter in a multi-resolution mode, the spindles were detected from the increase of the IV band power; the sampling rate of the device determined the filter characteristics. The staging of polysomnographic studies was made by an expert according AASM (American Academy of Sleep Medicine) and then processed by the filter to get the index of sleep spindles before-and-after CPAP during stage II as well as the relationship between fast and slow powers from the EEG signal. An increase in the power of the slow waves vs. fast activity was observed in all the cases as a feature of better sleep. The neuroprotective effect described in previous research works regarding the density of the sleep spindles seems to be detected in patients improving their sleep quality after the correction of the apnea-hypopnea syndrome using CPAP.Fil: Smurra, Marcela. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos Dr. Enrique Tornú; ArgentinaFil: Blanco, Susana Alicia Ana. Universidad de Belgrano. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Eguiguren, Veronica. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos Dr. Enrique Tornú; ArgentinaFil: Di Risio, Cecilia Diana. Universidad de Belgrano. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Artifact Removal Methods in EEG Recordings: A Review

To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods

The spatiotemporal pattern of the human electroencephalogram at sleep onset after a period of prolonged wakefulness

During the sleep onset (SO) process, the human electroencephalogram (EEG) is characterized by an orchestrated pattern of spatiotemporal changes. Sleep deprivation (SD) strongly affects both wake and sleep EEG, but a description of the topographical EEG power spectra and oscillatory activity during the wake-sleep transition after a period of prolonged wakefulness is still missing. The increased homeostatic sleep pressure should induce an earlier onset of sleep-related EEG oscillations. The aim of the present study was to assess the spatiotemporal EEG pattern at SO following SD. A dataset of a previous study was analyzed. We assessed the spatiotemporal EEG changes (19 cortical derivations) during the SO (5 min before vs. 5 min after the first epoch of Stage 2) of a recovery night after 40 h of SD in 39 healthy subjects, analyzing the EEG power spectra (fast Fourier transform) and the oscillatory activity [better oscillation (BOSC) detection method]. The spatiotemporal pattern of the EEG power spectra mostly confirmed the changes previously observed during the wake-sleep transition at baseline. The comparison between baseline and recovery showed a wide increase of the post- vs. pre-SO ratio during the recovery night in the frequency bins 10 Hz. We found a predominant alpha oscillatory rhythm in the pre-SO period, while after SO the theta oscillatory activity was prevalent. The oscillatory peaks showed a generalized increase in all frequency bands from delta to sigma with different predominance, while beta activity increased only in the fronto-central midline derivations. Overall, the analysis of the EEG power replicated the topographical pattern observed during a baseline night of sleep but with a stronger intensity of the SO-induced changes in the frequencies 10 Hz, and the detection of the rhythmic activity showed the rise of several oscillations at SO after SD that was not observed during the wake-sleep transition at baseline (e.g., alpha and frontal theta in correspondence of their frequency peaks). Beyond confirming the local nature of the EEG pattern at SO, our results show that SD has an impact on the spatiotemporal modulation of cortical activity during the falling-asleep process, inducing the earlier emergence of sleep-related EEG oscillations

Intelligent Bed

Cílem této práce je studium rys fyzické aktivity v denním cyklu "spánek - bdění", stejně jako studium změn spánku pod vlivem vnějších faktorů pomocí vyvinutého hardware - softwarového monitorovacího systému. K dosažení tohoto cíle byly řešeny následující úkoly: 1. Vyhodnocen charakter distribuce fyzické aktivity účastníků studie v noci a ve dne. 2. Byla určena charakteristika motorické aktivity těchto dvou účastníků. 3. Byly vyhodnoceny vztahy změn ukazatelů fyzické aktivity s rostoucími vnějšími ovlivňujícími factory. 4. Byla vyhodnocena účinnost aktigrafie během výzkumu.The purpose of this work is to study features of physical activity in the daily cycle "sleep - wakefulness" as well as changes in sleep under the influence of external factors using developed hardware - software monitoring system. In order to achieve this goal the following tasks were solved: 1. Evaluate the distribution's character of physical activity of the participants of the study at night and daytime. 2. Identify the characteristics of motor activity of these two participants. 3. Evaluate relations of changes of physical activity indicators with increasing external influencing factors. 4. Evaluate the efficiency of actigraphy method during the research

Midsagittal Jaw Movement Analysis for the Scoring of Sleep Apneas and Hypopneas

Given the importance of the detection and classification of sleep apneas and hypopneas (SAHs) in the diagnosis and the characterization of the SAH syndrome, there is a need for a reliable noninvasive technique measuring respiratory effort. This paper proposes a new method for the scoring of SAHs based on the recording of the midsagittal jaw motion (MJM, mouth opening) and on a dedicated automatic analysis of this signal. Continuous wavelet transform is used to quantize respiratory effort from the jaw motion, to detect salient mandibular movements related to SAHs and to delineate events which are likely to contain the respiratory events. The classification of the delimited events is performed using multilayer perceptrons which were trained and tested on sleep data from 34 recordings. Compared with SAHs scored manually by an expert, the sensitivity and specificity of the detection were 86.1% and 87.4%, respectively. Moreover, the overall classification agreement in the recognition of obstructive, central, and mixed respiratory events between the manual and automatic scorings was 73.1%. The MJM signal is hence a reliable marker of respiratory effort and allows an accurate detection and classification of SAHs

Rapid eye movements during sleep in mice: High trait-like stability qualifies rapid eye movement density for characterization of phenotypic variation in sleep patterns of rodents

<p>Abstract</p> <p>Background</p> <p>In humans, rapid eye movements (REM) density during REM sleep plays a prominent role in psychiatric diseases. Especially in depression, an increased REM density is a vulnerability marker for depression. In clinical practice and research measurement of REM density is highly standardized. In basic animal research, almost no tools are available to obtain and systematically evaluate eye movement data, although, this would create increased comparability between human and animal sleep studies.</p> <p>Methods</p> <p>We obtained standardized electroencephalographic (EEG), electromyographic (EMG) and electrooculographic (EOG) signals from freely behaving mice. EOG electrodes were bilaterally and chronically implanted with placement of the electrodes directly between the musculus rectus superior and musculus rectus lateralis. After recovery, EEG, EMG and EOG signals were obtained for four days. Subsequent to the implantation process, we developed and validated an Eye Movement scoring in Mice Algorithm (EMMA) to detect REM as singularities of the EOG signal, based on wavelet methodology.</p> <p>Results</p> <p>The distribution of wakefulness, non-REM (NREM) sleep and rapid eye movement (REM) sleep was typical of nocturnal rodents with small amounts of wakefulness and large amounts of NREM sleep during the light period and reversed proportions during the dark period. REM sleep was distributed correspondingly. REM density was significantly higher during REM sleep than NREM sleep. REM bursts were detected more often at the end of the dark period than the beginning of the light period. During REM sleep REM density showed an ultradian course, and during NREM sleep REM density peaked at the beginning of the dark period. Concerning individual eye movements, REM duration was longer and amplitude was lower during REM sleep than NREM sleep. The majority of single REM and REM bursts were associated with micro-arousals during NREM sleep, but not during REM sleep.</p> <p>Conclusions</p> <p>Sleep-stage specific distributions of REM in mice correspond to human REM density during sleep. REM density, now also assessable in animal models through our approach, is increased in humans after acute stress, during PTSD and in depression. This relationship can now be exploited to match animal models more closely to clinical situations, especially in animal models of depression.</p

EEG sleep stages identification based on weighted undirected complex networks

Sleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks. Methods each EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks. Results In this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by Naïve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals. Conclusions An average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard