The percentages for sperm motility, viability and intact acrosomes (PIA) were greater (P < 0.05) in Groups II and III as compared to the control group at the pre-freeze stage, while at the post-thaw stage, percentages of sperm motility, viability, PIA and HOS response were greater (P < 0.05) in Group II as compared with the control group and Group III.
Sleep stage percentages N1, N2, N3 (SWS="slow-wave sleep"), REM, arousals and respiratory events were visually scored.
There were not differences in sleep stage percentages and in respiratory indexes, such as apnea/hypopnea index (AHI) and oxygen desaturation index (ODI) between the two groups.
Even OSA-mediated fragmentation can be missed in routine clinical metrics (such as stage percentages).
Enhanced characterization of sleep architecture, compared with routine polysomnographic metrics such as stage percentages and sleep efficiency, may improve the predictive phenotyping of fragmented sleep.
Statistical analysis of sleep stage percentages typically assumes a Gaussian distribution, but some studies report mono-exponential distribution of sleep bout durations [19], [20].
Here, and elsewhere[14], [15], the pattern of stage stability is clearly different in patients with or without OSA – despite minimal differences apparent in summary statistics such as stage percentages.
Measurements that characterize sleep architecture according to state transition dynamics may capture the elusive concept of sleep continuity (or fragmentation) better than routine clinical statistics such as stage percentages or sleep efficiency.
Importantly, when sleep bout duration is considered in this dynamic sense (the distribution of time spent in a state), OSA was shown to alter sleep architecture despite unchanged "summary" PSG metrics, such as stage percentages or total sleep time[15].
