Obstructive sleep apnea (OSA) is a common sleep disorder that causes pauses of breathing due to repetitive obstruction of the upper airways of the respiratory system. The effect of this phenomenon can be observed in other physiological signals like the heart rate variability, oxygen saturation, and the respiratory effort signals. In this study, features from these signals were extracted from 50 control and 50 OSA patients from the Sleep Heart Health Study database and implemented for minute and subject classifications. A support vector machine (SVM) classifier was used with linear and second-order polynomial kernels. For the minute classification, the respiratory features had the highest sensitivity while the oxygen saturation gave the highest specificity. The polynomial kernel always had better performance and the highest accuracy of 82.4% (Sen: 69.9%, Spec: 91.4%) was achieved using the combined-feature classifier. For subject classification, the polynomial kernel had a clear improvement in the oxygen saturation accuracy as the highest accuracy of 95% was achieved by both the oxygen saturation (Sen: 100%, Spec: 90.2%) and the combined-feature (Sen: 91.8%, Spec: 98.0%). Further analysis of the SVM with other kernel types might be useful for optimizing the classifier with the appropriate features for an OSA automated detection algorithm.

/pdf/automated-recognition-of-obstructive-sleep-apnea-syndrome-a1zalnj11o.pdf

Automated Recognition of Obstructive Sleep Apnea Syndrome Using Support Vector Machine Classifier

Sleep: a comprehensive handbook

The risk of cardiovascular disease is known to be increased in obstructive sleep apnea syndrome (OSAS). Its mechanism can be explained by the observation that the sympathetic tone increases due to repetitive apneas accompanied by hypoxias and arousals during sleep. Heart rate variability (HRV) representing cardiac autonomic function is mediated by respiratory sinus arrhythmia, baroreflex-related fluctuation, and thermoregulation-related fluctuation. We evaluated the heart rate variability of OSAS patients during night to assess their relationship with the severity of the symptoms. We studied overnight polysomnographies of 59 male untreated OSAS patients with moderate to severe symptoms (mean age 45.4+/- 11.7 yr, apnea-hypopnea index [AHI]=43.2+/-23.4 events per hour, and AHI >15). Moderate (mean age 47.1+/-9.4 yr, AHI=15-30, n=22) and severe (mean age 44.5 +/-12.9 yr, AHI >30, n=37) OSAS patients were compared for the indices derived from time and frequency domain analysis of HRV, AHI, oxygen desaturation event index (ODI), arousal index (ArI), and sleep parameters. As a result, the severe OSAS group showed higher mean powers of total frequency (TF) (p=0.012), very low frequency (VLF) (p= 0.038), and low frequency (LF) (p=0.002) than the moderate OSAS group. The LF/HF ratio (p=0.005) was higher in the severe group compared to that of the moderate group. On the time domain analysis, the HRV triangular index (p=0.026) of severe OSAS group was significantly higher. AHI was correlated best with the LF/HF ratio (r(p))=0.610, p<0.001) of all the HRV indices. According to the results, the frequency domain indices tended to reveal the difference between the groups better than time domain indices. Especially the LF/HF ratio was thought to be the most useful parameter to estimate the degree of AHI in OSAS patients.

/pdf/correlation-between-the-severity-of-obstructive-sleep-apnea-3327b3xtt0.pdf

Correlation between the severity of obstructive sleep apnea and heart rate variability indices.

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 reflect 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 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.

/pdf/cardiac-variability-and-heart-rate-increment-as-a-marker-of-4h88d24g2z.pdf

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

OBSTRUCTIVE SLEEP APNEA SYNDROME (OSAS) IS A MAJOR HEALTHCARE PROBLEM AFFECTING AT LEAST 4% OF MEN AND 2% OF WOMEN.1 FIFTEEN million people in the United States alone are believed to have sleep apnea,2 but currently an estimated 80% to 90% of moderate-to-severe OSAS is undiagnosed.3

OSAS is a recognized independent risk factor for the development of cardiovascular diseases.4 Hypertension is found in up to 50% of patients with OSAS, and there is growing evidence supporting an association with ischemic heart disease, stroke, heart failure, atrial fibrillation, and cardiac sudden death.5,6 Long-term observational studies of effectively treated OSAS patients have also shown a significant benefit in reducing cardiovascular mortality and non-fatal cardiovascular events7–9 when compared to untreated patients. Growing recognition of the significance of OSAS has increased pressure on specialist sleep facilities, and in many countries resource constraints have restricted access to in-laboratory polysomnography.10

The diagnosis of OSAS requires the combined assessment of relevant clinical features and the objective demonstration of abnormal breathing during sleep; current evidence indicates that basing the diagnosis on either feature alone is unreliable.11–13 Thus, there is considerable interest in limited diagnostic tools that can be used by sleep physicians as part of an overall strategy to improve access to OSAS diagnosis and treatment.14 In this report we present a system that addresses one component of diagnosing OSAS, namely objective measurement of OSA.

We have previously developed a combined single-channel electrocardiogram (ECG) and oximetry analysis system for apnea detection.15 In a retrospective study of 125 patients with suspected sleep apnea, the system achieved a sensitivity and specificity of 96.5% and 95.0%, and positive and negative likelihood ratios of 19.30 and 0.04.

The present study prospectively validates the performance of the proposed automated ECG-oximetry system by comparing its performance with simultaneous diagnostic polysomnography in subjects with suspected OSAS. The ability of the system to (a) identify apnea events on an epoch-by-epoch basis and (b) identify or exclude subjects with clinically significant sleep apnea was evaluated. ECG and oximetry measurements were obtained using a portable combined Holter-oximeter to establish the practicality of the recorder and associated software as a portable automated assessment tool.

The study was conducted and the results reported with reference to the recommended methodology for study design and measurement of agreement between polysomnography and portable monitors set out in the AASM/ACCP/ATS joint review16 and its companion article.17

/pdf/a-portable-automated-assessment-tool-for-sleep-apnea-using-a-350ggi8a8i.pdf

A portable automated assessment tool for sleep apnea using a combined Holter-oximeter.

Many sleep centres employ a preliminary screening test in order to reduce the number of polysomnographies required in the routine diagnosis of the sleep apnoea/hypopnoea syndrome (SAHS). We investigated the combination ofheart rate and oximetry information as a means of performing this test. A retrospective study of 100 patients with suspected SAHS was made. All patients had in-hospital polysomnography on one night. We estimated the number of respiratory event-related arousals by counting the number of autonomic arousals (assessed on the basis of changes in the heart interbeat interval) that were coincident with a rise in oximetry. The hourly index of such events was denoted the 'cardiac-oximetry disturbance index' (CODI). The median apnoea/hypopnoea index (AHI) was 16.5 (range 1.0-93.6) h - 1 . The CODI correlated significantly with the AHI (Spearman correlation coefficient r s = 0.88, P < 0.01), and the area (′ standard error) under the receiver operating characteristic (ROC) was 0.94 ′ 0.05. Oximetry alone (based on 4% dips) was a less effective screening test (r s = 0.80. P < 0.01; area under ROC 0.83 ′ 0.06). Using 2% dips in oximetry offered comparable performance with the CODI (r s = 0.91, P < 0.01; area under ROC 0.93 ′ 0.04). The CODI was better correlated with the electroencephalograph arousal index (r s = 0.84, P < 0.01) than was oximetry (2% dips, r s =11.57. P < 0.01). The CODI algorithm also offers an informal measure of self-validation: a large discrepancy between the number of autonomic arousals and the number of rises in oximetry indicates the presence of autonomic arousals without changes in oximetry (or vice versa). This self-validation mechanism identified several patients in this study, and may be useful in identifying sleep disruption due to chronic pain or other causes.

Combined index of heart rate variability and oximetry in screening for the sleep apnoea/hypopnoea syndrome.

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Combined index of heart rate variability and oximetry in screening for the sleep apnoea/hypopnoea syndrome.