Sentence examples for sleep dataset from inspiring English sources

In the proposed study, two filter based statistical feature selection methods viz., statistical t-test ranking with principal component analysis (PCA) and Separability & Correlation (SEPCOR) analysis are applied to identify patterns with high discrimination between wake and stage 1 sleep of a 8-channel (6 active +2 reference electrodes) electroencephalogram (EEG) sleep dataset.

Data on neocortical and hippocampal volumes were available for 14 species in our sleep dataset, and data on amygdalar volumes for 13 species.

The across-study repeatability of sleeping time estimates using these criteria was high (0.82, F15,24 = 12.67, [ 53]; data are from a larger sleep dataset [see Additional File 1]), indicating that studies using our data selection criteria recorded very similar sleep durations for a given species.

We collected a sleep deprivation dataset [GSE9441] [ 36] consisting of three strains of mice in two sleep states (sleeping control and sleep deprived for 6 h).

Here we develop a model to predict a patient's age based on large-scale and heterogeneous sleep EEG datasets.

In all datasets, sleep quality was highest at baseline when all participants were still students (Table 1).

Qualitatively, the functional connectivity estimated from dataset B (sleep) has much more structure than that estimated from dataset R (reaching).

We constructed a dataset of mammalian sleep durations (REM and NREM sleep times in hours/day) from an exhaustive search of the published literature [29; data available at http://www.bu.edu/phylogeny/index.html].

This analysis suggests that across the 10 hour range of sleep durations present in the dataset there is a 24-fold decline in levels of parasitism.

Analysis of a dataset for multiple sleep-wake traits led to previously undetected interactions (including the differential genetic control of number and duration of REM bouts), as well as possible shared genetic regulatory mechanisms for seemingly different unrelated sleep-wake traits (e.g., number of arousals and REM latency).

We used publicly available data from Trypanosoma brucei, causative agent of sleeping sickness, and an original dataset from Saccharum officinarum (sugarcane), an important biofuel source, to illustrate several points in typical metabolomics analysis sections.