ABSTRACT
PURPOSE: Newly developed algorithms putatively derive measures of sleep, wakefulness, and respiratory disturbance index (RDI) through detailed analysis of heart rate variability (HRV). Here, we establish levels of agreement for one such algorithm through comparative analysis of HRV-derived values of sleep-wake architecture and RDI with those calculated from manually scored polysomnographic (PSG) recordings. METHODS: Archived PSG data collected from 234 subjects who participated in a 3-day, 2-night study characterizing polysomnographic traits of chronic fatigue syndrome were scored manually. The electrocardiogram and pulse oximetry channels were scored separately with a novel scoring algorithm to derive values for wakefulness, sleep architecture, and RDI. RESULTS: Four hundred fifty-four whole-night PSG recordings were acquired, of which, 410 were technically acceptable. Comparative analyses demonstrated no difference for total minutes of sleep, wake, NREM, REM, nor sleep efficiency generated through manual scoring with those derived through HRV analyses. When NREM sleep was further partitioned into slow-wave sleep (stages 3-4) and light sleep (stages 1-2), values calculated through manual scoring differed significantly from those derived through HRV analyses. Levels of agreement between RDIs derived through the two methods revealed an R = 0.89. The Bland-Altman approach for determining levels of agreement between RDIs generated through manual scoring with those derived through HRV analysis revealed a mean difference of -0.7 +/- 8.8 (mean +/- two standard deviations). CONCLUSION: We found no difference between values of wakefulness, sleep, NREM, REM sleep, and RDI calculated from manually scored PSG recordings with those derived through analyses of HRV.
Subject(s)
Algorithms , Electrocardiography , Fatigue Syndrome, Chronic/physiopathology , Polysomnography , Pulmonary Ventilation/physiology , Signal Processing, Computer-Assisted , Sleep Apnea, Obstructive/physiopathology , Sleep Stages/physiology , Wakefulness/physiology , Case-Control Studies , Fatigue Syndrome, Chronic/diagnosis , Female , Heart Rate/physiology , Humans , Male , Sleep Apnea, Obstructive/diagnosis , SoftwareABSTRACT
Pressure-driven flow in microfluidic channels is characterized by a distribution of velocities. This distribution makes it difficult to implement conventional flow cytometry data analysis. We have demonstrated a method to measure velocity as an independent parameter when performing microfluidic flow cytometry. This method allows velocity-independent analysis of particles such as beads or cells, and allows flow cytometry analysis of extended objects, such as long DNA molecules. It allows accurate flow cytometry in transient and nonuniform flows. This general measurement method could be used in the future to measure the velocity of particles in a variety of existing microfluidic devices without the need for changes in their design.
