Sleep-Related Modulations of Heart Rate Variability, ECG, and Cardio-Respiratory Coupling

Thomas Penzel, Yaopeng Ma, Jan Krämer, Niels Wessel, Martin Glos, Ingo Fietze, Ronny P. Bartsch

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Background: Integrated physiological systems following regular rhythms are a prime example of biological oscillators. Such systems include the heart with an oscillatory activity on a time scale of 1-second and the circadian pacemaker leading to a close to 24 h sleep-wake rhythm. Other prominent physiological oscillations each characterized by specific time scales are brain waves, respiration, blood pressure and vascular activity, and sleep-stage transitions with non-REM/REM cycles. In the healthy organism, these oscillators interact with each other, and studying those interactions during physiological transitions and in patients with disorders helps to uncover and better understand the underlying mechanisms. Methods: In order to investigate the coupling of these oscillators, sleep studies with cardiorespiratory polysomnography are performed on persons with healthy sleep and with sleep disorders. Polysomnography includes the recording of the electrocardiogram (ECG), the sleep-electroencephalogram (EEG), respiration, blood pressure/pulse wave, oxygen saturation, and movement activity by means of electromyogram (EMG). The analysis is performed visually by sleep experts, and computer assisted with time domain, frequency domain, and non-linear methodologies. Results: The parameters obtained provide information on the regulation of the autonomic nervous system (ANS) during sleep. The ANS is regulated totally different during slow-wave (non-REM) and REM sleep. Beat-to-beat heart-rate variations allow us to estimate a scoring of sleep stages. To some degree it is possible to track transitions from wakefulness to sleep by solely analyzing heart-rate variations. ECG and heart rate analysis allow assessment of sleep disorders as well. Cyclical variations of heart rate combined with respiration-modulated changes in ECG morphology (amplitude of R wave and T wave) provides reliable detection of sleep-disordered breathing. Conclusions: The assessment of signals being easily accessible like ECG and heart rate can help to assess sleep, sleep stages and sleep disorders with an acceptable accuracy, even if reflecting physiological functions indirectly.

Original languageEnglish
Title of host publicationUnderstanding Complex Systems
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
StatePublished - 2021

Publication series

NameUnderstanding Complex Systems


  • Autonomic function
  • Cardiorespiratory coupling
  • Cardiovascular regulation
  • ECG
  • Heart rate variability
  • Sleep apnea
  • Sleep stages

All Science Journal Classification (ASJC) codes

  • Software
  • Computational Mechanics
  • Artificial Intelligence


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