Relationship between delta power and the electrocardiogram-derived cardiopulmonary spectrogram: possible implications for assessing the effectiveness of sleep.

OBJECTIVES: The physiologic relationship between slow-wave activity (SWA) (0-4 Hz) on the electroencephalogram (EEG) and high-frequency (0.1-0.4 Hz) cardiopulmonary coupling (CPC) derived from electrocardiogram (ECG) sleep spectrograms is not known. Because high-frequency CPC appears to be a biomarker of stable sleep, we tested the hypothesis that that slow-wave EEG power would show a relatively fixed-time relationship to periods of high-frequency CPC. Furthermore, we speculated that this correlation would be independent of conventional nonrapid eye movement (NREM) sleep stages. METHODS: We analyzed selected datasets from an archived polysomnography (PSG) database, the Sleep Heart Health Study I (SHHS-I). We employed the cross-correlation technique to measure the degree of which 2 signals are correlated as a function of a time lag between them. Correlation analyses between high-frequency CPC and delta power (computed both as absolute and normalized values) from 3150 subjects with an apnea-hypopnea index (AHI) of ≤5 events per hour of sleep were performed. RESULTS: The overall correlation (r) between delta power and high-frequency coupling (HFC) power was 0.40±0.18 (P=.001). Normalized delta power provided improved correlation relative to absolute delta power. Correlations were somewhat reduced in the second half relative to the first half of the night (r=0.45±0.20 vs r=0.34±0.23). Correlations were only affected by age in the eighth decade. There were no sex differences and only small racial or ethnic differences were noted. CONCLUSIONS: These results support a tight temporal relationship between slow wave power, both within and outside conventional slow wave sleep periods, and high frequency cardiopulmonary coupling, an ECG-derived biomarker of "stable" sleep. These findings raise mechanistic questions regarding the cross-system integration of neural and cardiopulmonary control during sleep.

Effect of mild, asymptomatic obstructive sleep apnea on daytime heart rate variability and impedance cardiography measurements.

Dysregulation of autonomic nervous system dynamics is important in the pathophysiology of cardiovascular risk in obstructive sleep apnea (OSA). Heart rate variability (HRV) and impedance cardiography measures can estimate autonomic activity but have not gained traction clinically. The hypothesis of this study was that even in a cohort of patients with mild, asymptomatic OSA without overt cardiovascular disease, daytime HRV metrics and impedance cardiography measurements of preejection period would demonstrate increased sympathetic and decreased parasympathetic modulation compared to matched controls. Obese subjects (body mass index ≥30 kg/m(2)) without any known cardiovascular or inflammatory co-morbidities were recruited from the community. Subjects underwent standard in-laboratory polysomnography followed by simultaneous electrocardiographic and impedance cardiographic recordings while supine, supine with paced breathing, and after standing. Seventy-four subjects were studied, and 59% had OSA (apnea-hypopnea index ≥10 events/hour), with a median apnea-hypopnea index of 25.8 events/hour. Subjects with OSA had significantly decreased daytime time- and frequency-domain HRV indexes, but not significantly different preejection periods, compared to controls. Apnea-hypopnea index was a significant independent predictor of time-domain HRV measures in all awake conditions, after controlling for age, gender, blood pressure, fasting cholesterol levels and glycosylated hemoglobin. In conclusion, these results demonstrate reductions in cardiac vagal modulation, as measured by multiple daytime time-domain markers of HRV, in patients with asymptomatic OSA compared to controls. Further prospective outcomes-based studies are needed to evaluate the applicability of these metrics for noninvasive screening of obese patients with asymptomatic OSA, before the onset of overt cardiovascular disease.

Classification algorithms for predicting sleepiness and sleep apnea severity.

Identifying predictors of subjective sleepiness and severity of sleep apnea are important yet challenging goals in sleep medicine. Classification algorithms may provide insights, especially when large data sets are available. We analyzed polysomnography and clinical features available from the Sleep Heart Health Study. The Epworth Sleepiness Scale and the apnea-hypopnea index were the targets of three classifiers: k-nearest neighbor, naive Bayes and support vector machine algorithms. Classification was based on up to 26 features including demographics, polysomnogram, and electrocardiogram (spectrogram). Naive Bayes was best for predicting abnormal Epworth class (0-10 versus 11-24), although prediction was weak: polysomnogram features had 16.7% sensitivity and 88.8% specificity; spectrogram features had 5.3% sensitivity and 96.5% specificity. The support vector machine performed similarly to naive Bayes for predicting sleep apnea class (0-5 versus >5): 59.0% sensitivity and 74.5% specificity using clinical features and 43.4% sensitivity and 83.5% specificity using spectrographic features compared with the naive Bayes classifier, which had 57.5% sensitivity and 73.7% specificity (clinical), and 39.0% sensitivity and 82.7% specificity (spectrogram). Mutual information analysis confirmed the minimal dependency of the Epworth score on any feature, while the apnea-hypopnea index showed modest dependency on body mass index, arousal index, oxygenation and spectrogram features. Apnea classification was modestly accurate, using either clinical or spectrogram features, and showed lower sensitivity and higher specificity than common sleep apnea screening tools. Thus, clinical prediction of sleep apnea may be feasible with easily obtained demographic and electrocardiographic analysis, but the utility of the Epworth is questioned by its minimal relation to clinical, electrocardiographic, or polysomnographic features.

ECG-derived cardiopulmonary analysis of pediatric sleep-disordered breathing.

BACKGROUND: The diagnosis of sleep-disordered breathing (SDB) and evaluation of sleep quality in the pediatric population is dependent on resource intensive attended polysomnography. An ECG-derived cardiopulmonary coupling sleep spectrogram (CPC) analysis previously described in adults can provide information about the severity of SDB and coupled interactions of sleep modulated autonomic drive and respiration. We hypothesized that CPC algorithm-derived metrics will correlate with nasal pressure-based apnea-hypopnea scoring in pediatric population. METHODS: A total of 63 subjects (mean 6.2 years; range 2-12 years) were analyzed by both CPC and conventional nasal flow and desaturation scoring obtained during cardiorespiratory recordings. The characteristics of CPC indices and correlation with conventional SDB scoring were computed. RESULTS: High-frequency coupling (HFC), the CPC marker of stable sleep state, is reduced in proportion to SDB. The HFC durations are negatively correlated with the nasal flow-derived respiratory disturbance index (RDI), a CPC-derived RDI (CPC-RDI), and the 3% oxygen desaturation index (correlation coefficient -0.60, -0.78 and -0.54, respectively). CPC-RDI has a strong positive correlation with the conventional nasal-flow RDI (correlation coefficient 0.70). In this group with a mean nasal-flow RDI 36.1/h, the percentage of correct CPC diagnosis was 85.7% in total, 40% in the non-severe group (10 subjects, RDI <20/h) and 94.3% in the severe group (53 subjects, RDI >20/h). CONCLUSIONS: ECG-derived sleep spectrogram metrics are correlated with nasal flow-derived respiratory abnormality in pediatric SDB. In suitable clinical contexts, this method may have screening utility and possibly allow tracking of treatment effects, specifically in the children with severe SDB.

Sleep state instabilities in major depressive disorder: Detection and quantification with electrocardiogram-based cardiopulmonary coupling analysis.

Sleep disruption is an important aspect of major depressive disorder but lacks an objective and inexpensive means of assessment. We evaluated the utility of electrocardiogram (ECG)-based cardiopulmonary coupling analysis to quantify physiologic sleep stability in patients with major depression. Relative to controls, unmedicated depressed patients had a reduction in high-frequency coupling, an index of stable sleep, an increase in low-frequency coupling, an index of unstable sleep, and an increase in very-low-frequency coupling, an index of wakefulness/REM sleep. The medicated depressed group showed a restoration of stable sleep to a level comparable with that of the control group. ECG-based cardiopulmonary coupling analysis may provide a simple, cost-efficient point-of-care method to quantify sleep quality/stability and to objectively evaluate the severity of insomnia in patients with major depression.

Mapping sleep using coupled biological oscillations.

Sleep and wake state have different influences on a variety of recordable signals that make up the polysomnogram. Conventional sleep stages are dependent on analysis of electroencephalogram (EEG) waveforms. Non-EEG approaches can provide a different view of sleep. One such example is the electrocardiogram (ECG) derived sleep spectrogram, which computes the coupling and coherence of heart rate variability and respiratory tidal volume influences on the ECG R wave. Novel insights into sleep physiology and pathology are available through this technique.

Heritability of abnormalities in cardiopulmonary coupling in sleep apnea: use of an electrocardiogram-based technique.

RATIONALE: Studies of the genetics of obstructive sleep apnea may be facilitated by identifying intermediate traits with high heritability that quantify etiological pathways, such as those related to respiratory control. Electrocardiogram (ECG)-based sleep spectrograms, measuring the coupling between respiratory modulation of ECG QRS-wave amplitude and heart rate variability, may provide measures of sleep state and ventilatory dynamics during sleep. We evaluated the familial aggregation of distinctive spectrographic biomarkers of unstable sleep, related to elevated-low frequency cardiopulmonary coupling (e-LFC), to assess their utility in genetic studies. METHODS: 622 participants from 137 families from the Cleveland Family Study underwent standardized polysomnography (PSG). From the ECG signal on the PSG, the interbeat interval time series and the corresponding ECG-derived respiratory signal were extracted, and the low frequency (0.01-0.1 Hz) component of their coupling was computed using a fully automated method. Narrow sense heritability of e-LFC was calculated using variance component methods. RESULTS: A spectral marker of abnormal low frequency cardiopulmonary coupling (e-LFC) demonstrated moderate correlation with apnea hypopnea index (AHI; r = 0.35, P < 0.0001). The heritability estimate for e-LFC, after adjusting for age and sex was 0.32 (P < 10-5) and remained unchanged after additionally adjusting for body mass index or AHI. In biological relatives of those with sleep apnea, a related marker of e-LFC was more prevalent than in controls (P = 0.05). CONCLUSIONS: Approximately 30% of the variability of e-LFC, measured from a continuous ECG during sleep, is explained by familial factors other than BMI. ECG-based spectrographic measures of cardiopulmonary coupling may provide novel phenotypes for characterizing subgroups of individuals with different propensities and genetic etiologies for sleep apnea or for other conditions associated with sleep fragmentation.

Prevalent hypertension and stroke in the Sleep Heart Health Study: association with an ECG-derived spectrographic marker of cardiopulmonary coupling.

STUDY OBJECTIVES: The electrocardiogram (ECG)-based sleep spectrogram generates a map of cardiopulmonary coupling based on heart rate variability and respiration derived from QRS amplitude variations. A distinct spectrographic phenotype, designated as narrow-band elevated low frequency coupling (e-LFC(NB)), has been associated with central apneas and periodic breathing and predicts sleep laboratory failure of continuous positive airway pressure therapy. This study assesses, at a population level, the associations of this spectrographic biomarker with prevalent cardiovascular disease using the Sleep Heart Health Study (SHHS)-I dataset. DESIGN: Retrospective analysis of the Sleep Heart Health Study-I dataset. SETTING: Laboratory for complex physiologic signals analysis. MEASUREMENTS AND RESULTS: The fully-automated ECG-derived sleep spectrogram technique was applied to 5247 (of the original 6441) polysomnograms from the SHHS-I. Associations were estimated with use of various drugs and pathologies including prevalent hypertension and cardiovascular and cerebrovascular disease. Increasing with age and more common in males, e-LFC(NB) is also associated with greater severity of sleep apnea and fragmented sleep. After adjustment for potential confounders, an independent association with prevalent hypertension and stroke was found. CONCLUSIONS: An ECG-derived spectrographic marker related to low frequency cardiopulmonary coupling is associated with greater sleep apnea severity. Whether this biomarker is solely a sign of more severe disease or whether it reflects primary alterations in sleep apnea pathophysiology (which may either cause or result from sleep apnea) is unknown. This ECG-based spectral marker is associated with a higher prevalence of hypertension and stroke.

Enhancement of sleep stability with Tai Chi exercise in chronic heart failure: preliminary findings using an ECG-based spectrogram method.

OBJECTIVE: To assess the effects of a 12-week Tai Chi exercise program on sleep using the sleep spectrogram, a method based on a single channel electrocardiogram (ECG)-derived estimation of cardiopulmonary coupling, previously shown to identify stable and unstable sleep states. METHODS: We retrospectively analyzed 24-h continuous ECG data obtained in a clinical trial of Tai Chi exercise in patients with heart failure. Eighteen patients with chronic stable heart failure, left ventricular ejection fraction

Effects of chronic moderate alcohol consumption and novel environment on heart rate variability in primates (Macaca fascicularis).

RATIONALE: The effects of chronic moderate alcohol consumption on cardiac function are not understood. Acute stress may affect cardiac function by shifting autonomic cardiac regulation in favor of the sympathetic nervous system. Although alcohol consumption often increases at times of stress, the interactive effects of stress and chronic moderate alcohol consumption on cardiac regulation have not been studied. OBJECTIVES AND METHODS: The objective was to assess the effects of long-term (1-2 years) moderate (a two-drink/day equivalent, 5 days/week) alcohol consumption on heart rate (HR) variability under normal and acutely stressful conditions in small stable groups of ovariectomized adult cynomolgus monkeys (Macaca fascicularis). Monkeys were trained to voluntarily drink their daily alcohol dose (<30 min), and blood levels were determined an hour later. The animals were acutely stressed by removal from the home cage to a novel environment for 30 min. HR in freely moving subjects was recorded via telemetry in the home cage and the novel environment. RESULTS: Acute stress increased HR, decreased HR variability, and decreased the high frequency component of the power spectrum suggesting reduced parasympathetic cardiac modulation. Chronic moderate alcohol consumption decreased HR variability and the low frequency components of the power spectrum. When stressed, monkeys with a history of chronic moderate alcohol consumption had higher HRs than the controls. CONCLUSIONS: HR dynamics in monkeys rapidly respond to acute stress. Chronic moderate alcohol consumption may be deleterious to cardiac function. HR response to stress may be exaggerated when accompanied by a history of chronic moderate alcohol consumption.

Short-term fasting-induced autonomic activation and changes in catecholamine levels are not mediated by changes in leptin levels in healthy humans.

OBJECTIVE: In animal models, the adipocyte-secreted hormone leptin increases energy expenditure by increasing sympathetic outflow but its role in humans remains to be elucidated. We evaluated whether inducing hypoleptinaemia (with and without administration of leptin at replacement doses) for 3 days would influence catecholamine levels and sympathetic and parasympathetic activity in healthy humans. METHODS: We studied six normal-weight subjects in the General Clinical Research Center (GCRC) under three conditions: baseline fed state (control study) and two 72-h fasting studies (to decrease leptin levels), with administration of either placebo or replacement-dose recombinant methionyl human leptin (r-metHuLeptin) in a randomized, double-blind fashion. In each condition, 24-h urinary catecholamine levels, heart rate and heart rate variability (HRV), a standard tool for assessing cardiac autonomic modulation, were measured. RESULTS: Study parameters remained stable during the control condition and the baseline assessment of all three studies. In response to 72-h fasting, which decreased serum leptin levels by 80%, 24-h urinary norepinephrine and dopamine levels and heart rate increased while cardiac vagal modulation decreased (all P < 0.05). Replacement-dose r-metHuLeptin to keep leptin levels within the physiological range during fasting did not alter fasting-associated changes in heart rate, catecholamine levels or cardiac vagal tone. CONCLUSIONS: The findings of this controlled, interventional study indicate that changes in heart rate, catecholamine levels and cardiac vagal modulation associated with 72-h fasting are independent of regulation by leptin. Thus, changes in leptin levels within the physiological range do not seem to play a role in regulating autonomic function during short-term starvation in healthy humans.

Differentiating obstructive from central and complex sleep apnea using an automated electrocardiogram-based method.

STUDY OBJECTIVES: Complex sleep apnea is defined as sleep disordered breathing secondary to simultaneous upper airway obstruction and respiratory control dysfunction. The objective of this study was to assess the utility of an electrocardiogram (ECG)-based cardiopulmonary coupling technique to distinguish obstructive from central or complex sleep apnea. DESIGN: Analysis of archived polysomnographic datasets. SETTING: A laboratory for computational signal analysis. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The PhysioNet Sleep Apnea Database, consisting of 70 polysomnograms including single-lead ECG signals of approximately 8 hours duration, was used to train an ECG-based measure of autonomic and respiratory interactions (cardiopulmonary coupling) to detect periods of apnea and hypopnea, based on the presence of elevated low-frequency coupling (e-LFC). In the PhysioNet BIDMC Congestive Heart Failure Database (ECGs of 15 subjects), a pattern of "narrow spectral band" e-LFC was especially common. The algorithm was then applied to the Sleep Heart Health Study-I dataset, to select the 15 records with the highest amounts of broad and narrow spectral band e-LFC. The latter spectral characteristic seemed to detect not only periods of central apnea, but also obstructive hypopneas with a periodic breathing pattern. Applying the algorithm to 77 sleep laboratory split-night studies showed that the presence of narrow band e-LFC predicted an increased sensitivity to induction of central apneas by positive airway pressure. CONCLUSIONS: ECG-based spectral analysis allows automated, operator-independent characterization of probable interactions between respiratory dyscontrol and upper airway anatomical obstruction. The clinical utility of spectrographic phenotyping, especially in predicting failure of positive airway pressure therapy, remains to be more thoroughly tested.

An electrocardiogram-based technique to assess cardiopulmonary coupling during sleep.

STUDY OBJECTIVES: To evaluate a new automated measure of cardiopulmonary coupling during sleep using a single-lead electrocardiographic signal. DESIGN: Using training and test datasets of 35 polysomnograms each, we assessed the correlations of an electrocardiogram-based measure of cardiopulmonary interactions with respect to standard sleep staging, as well as to the cyclic alternating pattern classification. The pattern of coupling in 15 healthy individuals was also assessed. SETTING: American Academy of Sleep Medicine Accredited Sleep Disorders Center. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: From a continuous, single-lead electrocardiogram, we extracted both the normal-to-normal sinus interbeat interval series and a corresponding electrocardiogram-derived respiration signal. Employing Fourier-based techniques, the product of the coherence and cross-power of these 2 simultaneous signals was used to generate a spectrographic representation of cardiopulmonary coupling dynamics during sleep. This technique shows that non-rapid eye movement sleep in adults demonstrates spontaneous abrupt transitions between high- and low-frequency cardiopulmonary coupling regimes, which have characteristic electroencephalogram, respiratory, and heart-rate variability signatures in both health and disease. Using the kappa statistic, agreement with standard sleep staging was poor (training set 62.7%, test set 43.9%) but higher with cyclic alternating pattern scoring (training set 74%, test set 77.3%). CONCLUSIONS: A sleep spectrogram derived from information in a single-lead electrocardiogram can be used to dynamically track cardiopulmonary interactions. The 2 distinct (bimodal) regimes demonstrate a closer relationship with visual cyclic alternating pattern and non-cyclic alternating pattern states than with standard sleep stages. This technique may provide a complementary approach to the conventional characterization of graded non-rapid eye movement sleep stages.

Heart rate dynamics during three forms of meditation.

OBJECTIVE: This study was designed to quantify and compare the instantaneous heart rate dynamics and cardiopulmonary interactions during sequential performance of three meditation protocols with different breathing patterns. BACKGROUND: We analyzed beat-to-beat heart rate and continuous breathing signals from 10 experienced meditators (4 females; 6 males; mean age 42 years; range 29-55 years) during three traditional interventions: relaxation response, breath of fire, and segmented breathing. RESULTS: Heart rate and respiratory dynamics were generally similar during the relaxation response and segmented breathing. We observed high amplitude, low frequency (approximately 0.05-0.1 Hz) oscillations due to respiratory sinus arrhythmia during both the relaxation response and segmented breathing, along with a significantly (p<0.05) increased coherence between heart rate and breathing during these two maneuvers when compared to baseline. The third technique, breath of fire, was associated with a different pattern of response, marked by a significant increase in mean heart rate with respect to baseline (p<0.01), and a significant decrease in coherence between heart rate and breathing (p<0.05). CONCLUSIONS: These findings suggest that different meditative/breathing protocols may evoke common heart rate effects, as well as specific responses. The results support the concept of a "meditation paradox," since a variety of relaxation and meditative techniques may produce active rather than quiescent cardiac dynamics, associated with prominent low frequency heart rate oscillations or increases in mean resting heart rate. These findings also underscore the need to critically assess traditional frequency domain heart rate variability parameters in making inferences about autonomic alterations during meditation with slow breathing.

Diurnal and ultradian dynamics of serum adiponectin in healthy men: comparison with leptin, circulating soluble leptin receptor, and cortisol patterns.

Adiponectin is an abundant serum adipokine secreted exclusively from differentiated adipocytes, which plays an important role in regulating insulin sensitivity. The dynamics of circulating adiponectin concentrations have yet to be systematically investigated. We sought to determine whether serum adiponectin levels exhibit diurnal or ultradian rhythms in healthy normal-weight men and to compare the 24-h profile of adiponectin fluctuations with those of leptin, leptin-binding protein (sOB-R), and cortisol. We collected blood samples at 15-min intervals over 24 h from six subjects receiving an isocaloric diet, and we measured adiponectin, leptin, sOB-R, and cortisol levels. Fourier and cross-correlation analyses were performed on these time series to study diurnal variations, and the Cluster7 program was used for pulsatility analysis. Circulating adiponectin and sOB-R levels exhibited ultradian pulsatility as well as a diurnal variation with a significant decline at night, reaching a nadir in the early morning. The 24-h variations of serum adiponectin and sOB-R were nearly identical and followed those of cortisol after a few hours, but were out-of-phase with leptin diurnal rhythms. These data suggest that adiponectin and sOB-R levels might be influenced by common regulatory factors and challenge the notion that cortisol may have a direct inhibitory effect on adiponectin in humans.

Quantifying fractal dynamics of human respiration: age and gender effects.

We sought to quantify the fractal scaling properties of human respiratory dynamics and determine whether they are altered with healthy aging and gender. Continuous respiratory datasets (obtained by inductive plethysmography) were collected from 40 healthy adults (10 young men, 10 young women, 10 elderly men, and 10 elderly women) during 120 min of spontaneous breathing. The interbreath interval (IBI) time series were extracted by a new algorithm and fractal scaling exponents that quantify power-law correlations were computed using detrended fluctuation analysis. Under supine, resting, and spontaneous breathing conditions, both healthy young and elderly subjects had scaling exponents for the IBI time series that indicate long-range (fractal) correlations across multiple time scales. Furthermore, the scaling exponents (mean +/- SD) for the IBI time series were significantly (p < 0.03) lower (indicating decreased correlations) in the healthy elderly male (0.60 +/- 0.08) compared to the young male (0.68 +/- 0.07), young female (0.70 +/- 0.07), and elderly female (0.67 +/- 0.06) subjects. These results provide evidence for fractal organization in physiologic human breathing cycle dynamics, and for their degradation in elderly men. These findings may have implications for modeling integrated respiratory control mechanisms, quantifying their changes in aging or disease, and assessing the outcome of interventions aimed toward restoring normal physiologic respiratory dynamics.