Background:It is increasingly popular to use heart-rate variability (HRV) to tailor training for athletes. A time-efficient method is HRV assessment during deep sleep.Aim:To validate the selection of deep-sleep segments identified by RR intervals with simultaneous electroencephalography (EEG) recordings and to compare HRV parameters of these segments with those of standard morning supine measurements.Methods:In 11 world-class alpine skiers, RR intervals were monitored during 10 nights, and simultaneous EEGs were recorded during 2–4 nights. Deep sleep was determined from the HRV signal and verified by delta power from the EEG recordings. Four further segments were chosen for HRV determination, namely, a 4-h segment from midnight to 4 AM and three 5-min segments: 1 just before awakening, 1 after waking in supine position, and 1 in standing after orthostatic challenge. Training load was recorded every day.Results:A total of 80 night and 68 morning measurements of 9 athletes were analyzed. Good correspondence between the phases selected by RR intervals vs those selected by EEG was found. Concerning root-mean-squared difference of successive RR intervals (RMSSD), a marker for parasympathetic activity, the best relationship with the morning supine measurement was found in deep sleep.Conclusion:HRV is a simple tool for approximating deep-sleep phases, and HRV measurement during deep sleep could provide a time-efficient alternative to HRV in supine position.