2016
DOI: 10.3389/fphys.2016.00460
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Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography

Abstract: The cardiac component of cardio-respiratory polysomnography is covered by ECG and heart rate recordings. However, their evaluation is often underrepresented in summarizing reports. As complements to EEG, EOG, and EMG, these signals provide diagnostic information for autonomic nervous activity during sleep. This review presents major methodological developments in sleep research regarding heart rate, ECG, and cardio-respiratory couplings in a chronological (historical) sequence. It presents physiological and pa… Show more

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Cited by 139 publications
(112 citation statements)
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“…In this spectral analysis of HRV, one can clearly see the gradual formation and increase of RSA as a band around 0.25 Hz. Although the total activity is higher during awake state, the activity in the RSA band stands out during the non-REM sleep -falling asleep qualitatively resembles a phase transition normally studied in physics (Bartsch et al, 2012;Penzel et al, 2016). This process is reversed when entering the REM stage (from minute 63 on).…”
Section: Introductionmentioning
confidence: 57%
“…In this spectral analysis of HRV, one can clearly see the gradual formation and increase of RSA as a band around 0.25 Hz. Although the total activity is higher during awake state, the activity in the RSA band stands out during the non-REM sleep -falling asleep qualitatively resembles a phase transition normally studied in physics (Bartsch et al, 2012;Penzel et al, 2016). This process is reversed when entering the REM stage (from minute 63 on).…”
Section: Introductionmentioning
confidence: 57%
“…The cycling of sympathetic/parasympathetic tone during OSA has a unique influence on sleep‐time HRV and results in recurring variation of the heart rate . This influence can be detected using time‐domain or frequency domain methods and is the basis of algorithms for detecting sleep apnea events and estimating sleep apnea severity using the ECG alone . HRV can also be used to make predictions in populations without known cardiovascular disease with low HRV associated with an increased risk of a first cardiovascular event …”
Section: Enhanced Psg Signal Analysis Methodsmentioning
confidence: 99%
“…Breathing results in modulation in the amplitude of T and R waves. [31] analyzed that sleep disorders can be predicted using Morphology of ECG and heart rate by cardio-pulmonary coupling. Multi resolution wavelet transforms are used by [32] to separate ECG into alpha, beta, delta and theta spectral components and these coefficients were fed as input into neural networks.…”
Section: Based On Ecgmentioning
confidence: 99%