316
T. Penzel et al.
Snyder’s physiological investigations, heart rate continuously falls with sleep depth
and reaches lowest values in deep sleep [37]. In parallel, during deep sleep, sympathicotonic nervous activity falls to very low levels, and the parasympathetic nervous
system dominates [38]. In contrast, during REM sleep, the brain and other physiological systems show higher levels of activity (comparable to relaxed wakefulness)
associated with increased sympathicotonia [38]. Consequently, the mean heart rate
and HRV during REM are higher compared to light and deep sleep. In addition to
these sleep-stage influences, heart rate and HRV are subject to circadian modulation
and are also evidently influenced by prior extensive phases of wakefulness or sleep
deprivation leading to increased sleep drive [12].
Variations in heart rate obtained from nocturnal ECG recordings can detect
changes in the functions of the sympathetic and parasympathetic nervous systems,
and analysis of alterations in this context can be performed by classical techniques
such as spectral analysis of HRV [1]. Nowadays, it is common knowledge that the
spectral power in the low-frequency range (LF, 0.04–0.15 Hz) mostly relates to
sympathetic activity, whereas the high-frequency range (HF, 0.15–0.4 Hz) is associated with respiration and the activity of the parasympathetic nervous system (HRV
[40]). Discussion on the significance of very low frequencies (VLF, below 0.04 Hz)
is still taking place. Here, in conjunction with sleep-related breathing disorders, these
frequencies play a major role as pointed out in a recent systematic review [8].
Based on spectral power and other statistical methods of HRV analysis, sleep
stages can be estimated through the differences in autonomic nervous system regulation. Furthermore, up to some degree, it is possible to track transitions from wakefulness to sleep by analysis of heart-rate variations alone. In addition, an ECG during
cardiorespiratory polysomnography enables the monitoring of other vital functions
during nocturnal sleep studies [27] because it is sensitive enough to detect bradycardia or tachycardia, paroxysmal atrial fibrillation, AV block and in some cases
nocturnal coronary ischemia [7]. ECG also enables initial evaluation of nocturnal
arrhythmia in the sense of HRV and ectopic beats [7]. As a result, typical procedures
involve the recording of only one single-channel ECG, which can provide support
for a more comprehensive examination by multi-channel ECG diagnosis or by longterm ECG. ECG and HRV analysis allow the assessment of selected sleep disorders
as well. For example, sleep disordered breathing can be detected reliably by studying
cyclical variation of heart rate combined with respiration-modulated changes in ECG
morphology (amplitude of R wave and T wave).
20.3 Non-linear Analysis of Heart Rate Variability
Attempts to identify sleep stages and sleep apnea on the basis of heart rate and
with the help of computer-aided techniques, have encountered problems because
of the non-stationary, intermittent characteristics of heart rate interval recordings
that violate the preconditions of classical frequency-analysis procedures. This has
led to consideration and trial of new techniques taken from statistical physics. These
T. Penzel et al.
Snyder’s physiological investigations, heart rate continuously falls with sleep depth
and reaches lowest values in deep sleep [37]. In parallel, during deep sleep, sympathicotonic nervous activity falls to very low levels, and the parasympathetic nervous
system dominates [38]. In contrast, during REM sleep, the brain and other physiological systems show higher levels of activity (comparable to relaxed wakefulness)
associated with increased sympathicotonia [38]. Consequently, the mean heart rate
and HRV during REM are higher compared to light and deep sleep. In addition to
these sleep-stage influences, heart rate and HRV are subject to circadian modulation
and are also evidently influenced by prior extensive phases of wakefulness or sleep
deprivation leading to increased sleep drive [12].
Variations in heart rate obtained from nocturnal ECG recordings can detect
changes in the functions of the sympathetic and parasympathetic nervous systems,
and analysis of alterations in this context can be performed by classical techniques
such as spectral analysis of HRV [1]. Nowadays, it is common knowledge that the
spectral power in the low-frequency range (LF, 0.04–0.15 Hz) mostly relates to
sympathetic activity, whereas the high-frequency range (HF, 0.15–0.4 Hz) is associated with respiration and the activity of the parasympathetic nervous system (HRV
[40]). Discussion on the significance of very low frequencies (VLF, below 0.04 Hz)
is still taking place. Here, in conjunction with sleep-related breathing disorders, these
frequencies play a major role as pointed out in a recent systematic review [8].
Based on spectral power and other statistical methods of HRV analysis, sleep
stages can be estimated through the differences in autonomic nervous system regulation. Furthermore, up to some degree, it is possible to track transitions from wakefulness to sleep by analysis of heart-rate variations alone. In addition, an ECG during
cardiorespiratory polysomnography enables the monitoring of other vital functions
during nocturnal sleep studies [27] because it is sensitive enough to detect bradycardia or tachycardia, paroxysmal atrial fibrillation, AV block and in some cases
nocturnal coronary ischemia [7]. ECG also enables initial evaluation of nocturnal
arrhythmia in the sense of HRV and ectopic beats [7]. As a result, typical procedures
involve the recording of only one single-channel ECG, which can provide support
for a more comprehensive examination by multi-channel ECG diagnosis or by longterm ECG. ECG and HRV analysis allow the assessment of selected sleep disorders
as well. For example, sleep disordered breathing can be detected reliably by studying
cyclical variation of heart rate combined with respiration-modulated changes in ECG
morphology (amplitude of R wave and T wave).
20.3 Non-linear Analysis of Heart Rate Variability
Attempts to identify sleep stages and sleep apnea on the basis of heart rate and
with the help of computer-aided techniques, have encountered problems because
of the non-stationary, intermittent characteristics of heart rate interval recordings
that violate the preconditions of classical frequency-analysis procedures. This has
led to consideration and trial of new techniques taken from statistical physics. These