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

Screening of obstructive sleep apnea in patients who snore using a patch-type device with electrocardiogram and 3-axis accelerometer

STUDY OBJECTIVES

People with obstructive sleep apnea (OSA) remain undiagnosed because of the lack of easy and comfortable screening tools. Through this study, we aimed to compare the diagnostic accuracy of chest wall motion and cyclic variation of heart rate (CVHR) in detecting OSA by using a single-lead electrocardiogram (ECG) patch with a 3-axis accelerometer.

METHODS

In total, 119 patients who snore simultaneously underwent polysomnography with a single-lead ECG patch. Signals of chest wall motion and CVHR from the single-lead ECG patch were collected. The chest effort index (CEI) was calculated using the chest wall motion recorded by a 3-axis accelerometer in the device. The ability of CEI and CVHR indices in diagnosing moderate-to-severe OSA (apnea-hypopnea index ≥ 15) was compared using the area under the curve (AUC) by using the DeLong test.

RESULTS

CVHR detected moderate-to-severe OSA with 52.9% sensitivity and 94.1% specificity (AUC: 0.76, 95% confidence interval: 0.67-0.84, optimal cutoff: 21.2 events/h). By contrast, CEI identified moderate-to-severe OSA with 80% sensitivity and 79.4% specificity (AUC: 0.87, 95% confidence interval: 0.80-0.94, optimal cutoff: 7.1 events/h). CEI significantly outperformed CVHR regarding the discrimination ability for moderate-to-severe OSA (ΔAUC: 0.11, 95% confidence interval: 0.009-0.21, P = .032). For determining severe OSA, the performance of discrimination ability was greater (AUC = 0.90, 95% confidence interval: 0.85-0.95) when combining these two signals.

CONCLUSIONS

Both CEI and CVHR recorded from a patch-type device with ECG and a 3-axis accelerometer can be used to detect moderate-to-severe OSA. Thus, incorporation of CEI is helpful in the detection of sleep apnea by using a single-lead ECG with a 3-axis accelerometer.

Sleep apnea-hypopnea quantification by cardiovascular data analysis

Sleep disorders are a major risk factor for cardiovascular diseases. Sleep apnea is the most common sleep disturbance and its detection relies on a polysomnography, i.e., a combination of several medical examinations performed during a monitored sleep night. In order to detect occurrences of sleep apnea without the need of combined recordings, we focus our efforts on extracting a quantifier related to the events of sleep apnea from a cardiovascular time series, namely systolic blood pressure (SBP). Physiologic time series are generally highly nonstationary and entrap the application of conventional tools that require a stationary condition. In our study, data nonstationarities are uncovered by a segmentation procedure which splits the signal into stationary patches, providing local quantities such as mean and variance of the SBP signal in each stationary patch, as well as its duration . We analysed the data of 26 apneic diagnosed individuals, divided into hypertensive and normotensive groups, and compared the results with those of a control group. From the segmentation procedure, we identified that the average duration , as well as the average variance , are correlated to the apnea-hypoapnea index (AHI), previously obtained by polysomnographic exams. Moreover, our results unveil an oscillatory pattern in apneic subjects, whose amplitude is also correlated with AHI. All these quantities allow to separate apneic individuals, with an accuracy of at least . Therefore, they provide alternative criteria to detect sleep apnea based on a single time series, the systolic blood pressure.

Accuracy of ECG-based screening for sleep-disordered breathing: a survey of all male workers in a transport company

Purpose

Sleep-disordered breathing (SDB) is associated with increased risk for cardiovascular morbidity and mortality and for sleepiness-related accidents, but >75 % of the patients remain undiagnosed. We sought to determine the diagnostic accuracy of ECG-based detection of SDB when used for population-based screening.

Methods

All male workers, mostly truck drivers, of a transport company (n = 165; age, 43 ± 12 years) underwent standard attended overnight polysomnography. Cyclic variation of heart rate (CVHR), a characteristic pattern of heart rate associated with SDB, was detected from single-lead ECG signals during the polysomnography by a newly developed automated algorithm of autocorrelated wave detection with adaptive threshold (ACAT).

Results

Among 165 subjects, the apnea–hypopnea index (AHI) was ≥5 in 62 (38 %), ≥15 in 26 (16 %), and ≥30 in 16 (10 %). The number of CVHR per hour (CVHR index) closely correlated with AHI [r = 0.868 (95 % CI, 0.825–0.901)]. The areas under the receiver operating characteristic curves for detecting subjects with AHI ≥5, ≥15, and ≥30 were 0.796 (95 % CI, 0.727–0.855), 0.974 (0.937–0.993), and 0.997 (0.971–0.999), respectively. With a predetermined criterion of CVHR index ≥15, subjects with AHI ≥15 were identified with 88 % sensitivity and 97 % specificity (likelihood ratios for positive and negative test, 30.7 and 0.12). The classification performance was retained in subgroups of subjects with obesity, hypertension, diabetes mellitus, dyslipidemia, and decreased autonomic function.

Conclusions

The CVHR obtained by the ACAT algorithm may provide a useful marker for screening for moderate-to-severe SDB among apparently healthy male workers.

Sympathetic and catecholaminergic alterations in sleep apnea with particular emphasis on children

Sleep is involved in the regulation of major organ functions in the human body, and disruption of sleep potentially can elicit organ dysfunction. Obstructive sleep apnea (OSA) is the most prevalent sleep disorder of breathing in adults and children, and its manifestations reflect the interactions between intermittent hypoxia, intermittent hypercapnia, increased intra-thoracic pressure swings, and sleep fragmentation, as elicited by the episodic changes in upper airway resistance during sleep. The sympathetic nervous system is an important modulator of the cardiovascular, immune, endocrine and metabolic systems, and alterations in autonomic activity may lead to metabolic imbalance and organ dysfunction. Here we review how OSA and its constitutive components can lead to perturbation of the autonomic nervous system in general, and to altered regulation of catecholamines, both of which then playing an important role in some of the mechanisms underlying OSA-induced morbidities.