Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages.

Electroencephalography (EEG) is immediate and sensitive to neurological changes resulting from sleep stages and is considered a computing tool for understanding the association between neurological outcomes and sleep stages. EEG is expected to be an efficient approach for sleep stage prediction outside a highly equipped clinical setting compared with multimodal physiological signal-based polysomnography. This study aims to quantify the neurological EEG-biomarkers and predict five-class sleep stages using sleep EEG data. We investigated the three-channel EEG sleep recordings of 154 individuals (mean age of 53.8 ± 15.4 years) from the Haaglanden Medisch Centrum (HMC, The Hague, The Netherlands) open-access public dataset of PhysioNet. The power of fast-wave alpha, beta, and gamma rhythms decreases; and the power of slow-wave delta and theta oscillations gradually increases as sleep becomes deeper. Delta wave power ratios (DAR, DTR, and DTABR) may be considered biomarkers for their characteristics of attenuation in NREM sleep and subsequent increase in REM sleep. The overall accuracy of the C5.0, Neural Network, and CHAID machine-learning models are 91%, 89%, and 84%, respectively, for multi-class classification of the sleep stages. The EEG-based sleep stage prediction approach is expected to be utilized in a wearable sleep monitoring system.

Synthesis and electrochemical performance of 0.3Li2MnO3 x 0.7LiMO2 (M = Mn, Co, Ni) cathode materials for Li-lon batteries.

The Mn0.720Ni0.175Co0.105(OH)2 precursor was co-precipitated by the Couette-Taylor reactor. The 0.3Li2MnO3 x 0.7LiMn0.60Ni0.25Co0.15O2 of the high capacity cathode material for a Li-ion battery was synthesized according to the amount of lithium excess (5-20 mol.%). X-ray diffraction (XRD) and field emission-scanning electron microscopy (FE-SEM) were used to characterize the 0.3Li2MnO3 x 0.7Li-Mn0.60Ni0.25Co0.15O2. Based on the XRD patterns and FE-SEM images, the 5 and 10 mol.% lithium excess samples were observed for spinel structure. The 15 and 20 mol.% lithium excess samples were not observed for the structure. We can conclude that the spinel structure was made in 0.3Li2MnO3 x 0.7LiMn0.60-Ni0.25Co0.15O2, due to a lack of lithium. The discharge specific capacity of 5, 10, 15, and 20 mol.% lithium excess were measured at 216, 246, 262, and 261 mA h g(-1), respectively. Cyclic voltammograms show that the Li2MnO3 has a lower lithium influence than a spinel or layered structure. Based on these experiment results, we can conclude that the best Li source amount of the 0.3Li2MnO3 x 0.7LiMn0.60-Ni0.25Co0.15O2 synthesis is a 15 mol.% excess.