Sleep apnea

Use of high-frequency peak in spectral analysis of heart rate increment to improve screening of obstructive sleep apnoea.

Poupard L, Court-Fortune I, Pichot V, Chouchou F, Barthélémy JC, Roche F.
Novacor, Rueil Malmaison, France.
Sleep Breath. 2010 Nov 21
Purpose
Several studies have correlated the ratio of the very low frequency power spectral density of heart rate increment (%VLFI) with obstructive sleep apnoea syndrome (OSAS). However, patients with impaired heart rate variability may exhibit large variations of heart rate increment (HRI) spectral pattern and alter the screening accuracy of the method.
Methods
To overcome this limitation, the present study uses the high-frequency increment (HFI) peak in the HRI spectrum, which corresponds to the respiratory influence on RR variations over the frequency range 0.2 to 0.4 Hz. We evaluated 288 consecutive patients referred for snoring, observed nocturnal breathing cessation and/or daytime sleepiness. Patients were classified as OSAS if their apnoea plus hypopnoea index (AHI) during polysomnography exceeded 15 events per hour. Synchronized electrocardiogram Holter monitoring allowed HRI analysis.
Results
Using a %VLFI threshold >2.4% for identifying the presence of OSAS, sensitivity for OSAS was 74.9%, specificity 51%, positive predictive value 54.9% and negative predictive value 71.7% (33 false negative subjects). Using threshold for %VLFI >2.4% and HFI peak position >0.4 Hz, negative predictive value increased to 78.2% while maintaining specificity at 50.6%. Among 11 subjects with %VLFI <2.4% and HFI peak >0.4 Hz, nine demonstrated moderate to severe OSAS (AHI >30).
Conclusions
HFI represents a minimal physiological criterion for applying %VLFI by ensuring that heart rate variations are band frequency limited.

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Use of thoracic impedance sensors to screen for sleep-disordered breathing in patients with cardiovascular disease.

Poupard L, Mathieu M, Sartène R, Goldman M.
Novacor SA, 4 passage Saint Antoine, F-92500, Rueil Malmaison, France.
lpoupard@novacor.fr
Abstract
Screening patients for the possibility of sleep apnoea, one of the most common forms of sleep-disordered breathing, requires measurement of respiration. We propose a simple method to estimate the amplitude modulation of a respiratory tidal volume, using a semi-quantitative measure of respiration based on thoracic impedance (TI). Because respiratory volume changes may be accommodated by varying displacements of the rib cage (RC) and abdomen (AB), the latter produced by outward motion of the diaphragm, it is necessary for any useful measure of respiration to be closely related to both RC and AB displacements. Because the relative contributions of RC and AB displacements to respiratory tidal volume vary in different body positions, the present measurements were recorded from subjects in supine, and right and left lateral decubitus postures. We observed a clear linear relationship between TI and both RC and AB signals in all three body positions. There were no statistically significant differences between observed relationships between TI and AB and between TI and RC, and these relationships were independent of the body position. TI sensors appear to be a useful candidate for a simple method of screening for sleep apnoea, especially in a cardiology clinical setting. Further investigation is warranted for the refinement of algorithms to detect changes in amplitude modulation occurring with apnoeas and to remove artefacts due to gross body movements.

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Cardiac Variability and Heart-Rate Increment as a Marker of Sleep Fragmentation in Patients With a Sleep Disorder: a Preliminary Study

Emilia Sforza, MD, PhD(1); Vincent Pichot, PhD(2); Katerina Cervena MD(1); Jean Claude Barthélémy, MD, PhD(2); Frederic Roche, MD(2)
(1): Sleep laboratory, Department of Psychiatry, University Hospital, Geneva, Switzerland
(2): Service d’Exploration Fonctionnelle Cardiorespiratoire, University Hospital, Saint-Etienne, France.
Study Objectives
The ratio between the heart-rate increment to total power spectral density (%VLFI) has been introduced as a sensitive measure of sleep-related breathing disorders (SRBD). Since a complex interaction is present between sleep disorders and occurrence of arousals, we hypothesized that %VLFI and other indexes of heart-rate variability (HRV) measures refl ect the degree of sleep fragmentation.
Methods
The high- and low-frequency peaks from spectral analysis (FFT) of R-R intervals, the HRV changes using wavelet transform (WT), the geometric and time domain HRV, and the %VLFI were measured in 336 sleep studies performed in patients with insomnia, SRBD and restless legs syndrome/ periodic limb movement disorder (RLS/PLMD). The ability of HRV measures to assess sleep fragmentation was examined by correlation analysis and from the area under the receiver operating characteristic (ROC) curve.
Results
The ratio of low frequency to high frequency (LF/HF ratio) at the FFT and WT and the %VLFI were higher in patients with SRBD and RLS/PLMD, compared with patients with insomnia. These measures were related to the arousal (MA) index as well as to the apnea-hypopnea index, oxygen desaturations, and periodic leg movement index (p < .001). The presence of a sleep fragmentation defi ned as an MA index > 20 was well detected by the %VLFI (ROC area: 0.66 ± 0.03) and the LF/HF ratio at WT (ROC area: 0.66 ± 0.03).
Conclusion
The %VLFI and LF/HF ratio provide indirect measures of sleep fragmentation, suggesting that HRV measures during sleep assess more the associated sleep fragmentation than the presence of a specific sleep disorder.

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Cardiac Variability and Heart-Rate Increment as a Marker of Sleep Fragmentation in Patients With a Sleep Disorder: a Preliminary Study

Emilia Sforza, MD, PhD(1); Vincent Pichot, PhD(2); Katerina Cervena MD(1); Jean Claude Barthélémy, MD, PhD(2); Frederic Roche, MD(2)
(1): Sleep laboratory, Department of Psychiatry, University Hospital, Geneva, Switzerland
(2): Service d’Exploration Fonctionnelle Cardiorespiratoire, University Hospital, Saint-Etienne, France.
Study Objectives
The ratio between the heart-rate increment to total power spectral density (%VLFI) has been introduced as a sensitive measure of sleep-related breathing disorders (SRBD). Since a complex interaction is present between sleep disorders and occurrence of arousals, we hypothesized that %VLFI and other indexes of heart-rate variability (HRV) measures refl ect the degree of sleep fragmentation.
Methods
The high- and low-frequency peaks from spectral analysis (FFT) of R-R intervals, the HRV changes using wavelet transform (WT), the geometric and time domain HRV, and the %VLFI were measured in 336 sleep studies performed in patients with insomnia, SRBD and restless legs syndrome/ periodic limb movement disorder (RLS/PLMD). The ability of HRV measures to assess sleep fragmentation was examined by correlation analysis and from the area under the receiver operating characteristic (ROC) curve.
Results
The ratio of low frequency to high frequency (LF/HF ratio) at the FFT and WT and the %VLFI were higher in patients with SRBD and RLS/PLMD, compared with patients with insomnia. These measures were related to the arousal (MA) index as well as to the apnea-hypopnea index, oxygen desaturations, and periodic leg movement index (p < .001). The presence of a sleep fragmentation defi ned as an MA index > 20 was well detected by the %VLFI (ROC area: 0.66 ± 0.03) and the LF/HF ratio at WT (ROC area: 0.66 ± 0.03).
Conclusion
The %VLFI and LF/HF ratio provide indirect measures of sleep fragmentation, suggesting that HRV measures during sleep assess more the associated sleep fragmentation than the presence of a specific sleep disorder.

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The use of heart rate variability in detecting sleep disordered breathing in patients with heart failure

A Vazir 1*, M Dayer 1, RH Minnion 2, MR Cowie 1, PA Poole-Wilson 1, MJ Morrell 2 and AK Simonds 2. 1 Cardiac Medicine and 2 Clinical & Academic Unit of Sleep & Breathing, Royal Brompton Hospital, National Heart & lung Institute, Imperial college, London, UK.

Background
Sleep disordered breathing (SDB) is common in patients with congestive heart failure (CHF). In patients with OSA, the very low frequency (VLF) power spectral density of the interbeat interval increment, when corrected for the total power spectral density (%VLFI), is strongly associated with presence of obstructive apneas.
Aim
To determine if the %VLFI could be used to detect the presence of SDB in CHF patients.
Methods
20 male patients with CHF [age 62.1(11.7) yrs, NYHA 1.5(0.6), EF 39.5(12.7) %, MVO2 17.6(3.6) ml/Kg/min] were recruited. Patients with atrial fibrillation, ventricular pacing, excessive ventricular ectopy were excluded. All underwent polysomnography. SDB was defined as an AHI20 events/hr. Heart rate (HR) data was collected overnight using a Holter monitor and processed using an analysis program automated to calculate %VLFI (Novacor).
Results
9/20 had SDB. A receiver operator characteristic curve was constructed assuming a cut-off an AHI of 20 events/hr. The area under the curve was 0.869, and the asymptotic significance was 0.011. Setting the %VLFI at 2.33% yielded a sensitivity of 100% and a specificity of 64.3%. The positive predictive value was 54.5% and the negative predictive value 100%. There were no false negatives.

Summary
Spectral analysis of HR may be used as a rule out test for SDB in mild to moderate CHF. For health services assessment of HR variability may reduce demand on overburden sleep laboratories, by eliminating those unlikely to have SDB.

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Screening of obstructive sleep apnea syndrome by heart rate variability analysis.
 
Roche F. et al.
BACKGROUND
Enhanced nocturnal heart rate variability (HRV) has been evoked in sleep-related breathing disorders. However, its capacity to detect obstructive sleep apnea syndrome (OSAS) has not been systematically determined. Thus, we evaluated the discriminant power of HRV parameters in a first group of patients (G1) and validated their discriminant capacity in a second group (G2).
METHODS AND RESULTS
In G1, 39 of 91 patients (42.8%) were identified as diseased by polysomnography, as were 24 of 52 patients (46%) in G2. Time-domain HRV variables (SD of NN intervals [SDNN], mean of the standard deviations of all NN intervals for all consecutive 5-minute segments of the recording [SDNN index], square root of the mean of the sum of the squares of differences between adjacent normal RR intervals [r-MSSD], and SD of the averages of NN intervals in all 5-minute segments of the recording [SDANN]) were calculated for daytime and nighttime periods, as well as the differences between daytime and nighttime values (Delta[D/N]). Correlations between HRV variables and OSAS status were analyzed in G1 by use of receiver-operating characteristic (ROC) curves and logistic regression analysis. By ROC curve analysis, 7 variables were significantly associated with OSAS. After adjustment for other variables through multiple logistic regression analysis, Delta[D/N]SDNN index and Delta[D/N] r-MSSD remained significant independent predictors of OSAS, with ORs of 8.22 (95% CI, 3.16 to 21.4) and 2.86 (95% CI, 1.21 to 6.75), respectively. The classification and regression tree methodology demonstrated a sensitivity reaching 89.7% (95% CI, 73.7 to 97.7) with Delta[D/N] SDNN index and a specificity of 98.1% (95% CI, 86.4 to 100) with Delta[D/N] SDNN using appropriate thresholds. These thresholds, applied to G2, yielded a sensitivity of 83% using Delta[D/N] SDNN index and a specificity of 96.5% using Delta[D/N] SDNN.
CONCLUSIONS
Time-domain HRV analysis may represent an accurate and inexpensive screening tool in clinically suspected OSAS patients and may help focus resources on those at the highest risk.
Circulation 1999; 111:1411-1415