OBJECTIVES: This revised clinical practice guideline, intended for use by primary care clinicians, provides recommendations for the diagnosis and management of the obstructive sleep apnea syndrome (OSAS) in children and adolescents. This practice guideline focuses on uncomplicated childhood OSAS, that is, OSAS associated with adenotonsillar hypertrophy and/or obesity in an otherwise healthy child who is being treated in the primary care setting. METHODS: Of 3166 articles from 1999–2010, 350 provided relevant data. Most articles were level II–IV. The resulting evidence report was used to formulate recommendations. RESULTS AND CONCLUSIONS: The following recommendations are made. (1) All children/adolescents should be screened for snoring. (2) Polysomnography should be performed in children/adolescents with snoring and symptoms/signs of OSAS; if polysomnography is not available, then alternative diagnostic tests or referral to a specialist for more extensive evaluation may be considered. (3) Adenotonsillectomy is recommended as the first-line treatment of patients with adenotonsillar hypertrophy. (4) High-risk patients should be monitored as inpatients postoperatively. (5) Patients should be reevaluated postoperatively to determine whether further treatment is required. Objective testing should be performed in patients who are high risk or have persistent symptoms/signs of OSAS after therapy. (6) Continuous positive airway pressure is recommended as treatment if adenotonsillectomy is not performed or if OSAS persists postoperatively. (7) Weight loss is recommended in addition to other therapy in patients who are overweight or obese. (8) Intranasal corticosteroids are an option for children with mild OSAS in whom adenotonsillectomy is contraindicated or for mild postoperative OSAS.

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Diagnosis and Management of Childhood Obstructive Sleep Apnea Syndrome

A survey on ECG analysis

A novel method for detecting R-peaks in electrocardiogram (ECG) signal

QRS detection based on wavelet coefficients

Measurement of the heart rate (HR) variability from short and long-term electrocardiographic (ECG) recordings is a non-invasive method, which can used be as a tool to evaluate cardiac autonomic regulation. HR variability can give information about the sympathetic-parasympathetic autonomic balance. One important clinical application is to measure HR variability from patients suffering acute myocardial infarction (AMI). However, HR variability signals, R-R interval time series obtained from ambulatory ECG recordings contain in most of the cases different amount of artefacts. Artefacts appear due to disturbances of either physiological or technical origin. For instance, technical artefacts may result from poorly fastened electrodes or be due to motion artefacts. Ectopic beats and atrial fibrillation are examples of biological artefacts. Since the ectopic beats and other artifacts are common phenomena in the R-R interval time series, they make a reliable analysis of the HR variability difficult or even impossible. In conjunction with the increased usage of HR variability analyses, various studies have confirmed the need of different approaches to handle artefacts in the R-R interval time series. The editing process of the R-R interval time series has become an important part of the HR variability analysis. Several different editing and HR variability signal preprocessing methods have been introduced and tested for the artefact correction. Artifact correction can be performed with different methods such as deletion, various interpolation methods and different filtering systems. However, different editing methods can have different effects on the HR variability measures. The effects of editing is depended on the study setting including the editing method, HR variability measure, type of study population, length of the R-R interval time series.

Role of editing of R-R intervals in the analysis of heart rate variability

Accurate QRS detection is an important first step for the analysis of heart rate variability Algorithms based on the differentiated ECG are computationally efficient and hence ideal for real-time analysis of large datasets Here, we analyze traditional first-derivative based squaring function (Hamilton-Tompkins) and Hilbert transform-based methods for QRS detection and their modifications with improved detection thresholds On a standard ECG dataset, the Hamilton-Tompkins algorithm had the highest detection accuracy (9968% sensitivity, 9963% positive predictivity) but also the largest time error The modified Hamilton-Tompkins algorithm as well as the Hilbert transform-based algorithms had comparable, though slightly lower, accuracy; yet these automated algorithms present an advantage for real-time applications by avoiding human intervention in threshold determination The high accuracy of the Hilbert transform-based method compared to detection with the second derivative of the ECG is ascribable to its inherently uniform magnitude spectrum For all algorithms, detection errors occurred mainly in beats with decreased signal slope, such as wide arrhythmic beats or attenuated beats For best performance, a combination of the squaring function and Hilbert transform-based algorithms can be applied such that differences in detection will point to abnormalities in the signal that can be further analyzed

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Analysis of First-Derivative Based QRS Detection Algorithms

A paradox regarding the classic power spectral analysis of heart rate variability (HRV) is whether the characteristic high- (HF) and low-frequency (LF) spectral peaks represent stochastic or chaotic phenomena. Resolution of this fundamental issue is key to unraveling the mechanisms of HRV, which is critical to its proper use as a noninvasive marker for cardiac mortality risk assessment and stratification in congestive heart failure (CHF) and other cardiac dysfunctions. However, conventional techniques of nonlinear time series analysis generally lack sufficient sensitivity, specificity and robustness to discriminate chaos from random noise, much less quantify the chaos level. Here, we apply a ‘litmus test’ for heartbeat chaos based on a novel noise titration assay which affords a robust, specific, time-resolved and quantitative measure of the relative chaos level. Noise titration of running short-segment Holter tachograms from healthy subjects revealed circadian-dependent (or sleep/wake-dependent) heartbeat chaos that was linked to the HF component (respiratory sinus arrhythmia). The relative ‘HF chaos’ levels were similar in young and elderly subjects despite proportional age-related decreases in HF and LF power. In contrast, the near-regular heartbeat in CHF patients was primarily nonchaotic except punctuated by undetected ectopic beats and other abnormal beats, causing transient chaos. Such profound circadian-, age- and CHF-dependent changes in the chaotic and spectral characteristics of HRV were accompanied by little changes in approximate entropy, a measure of signal irregularity. The salient chaotic signatures of HRV in these subject groups reveal distinct autonomic, cardiac, respiratory and circadian/sleep-wake mechanisms that distinguish health and aging from CHF.

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Chaotic Signatures of Heart Rate Variability and Its Power Spectrum in Health, Aging and Heart Failure

Accurate processing of electrocardiogram (ECG) signals requires a sensitive and robust QRS detection method. In this study, three methods are quantitatively compared using a similar algorithm structure but applying different transforms to the differentiated ECG. The three transforms used are the Hilbert transformer, the squaring function, and a second discrete derivative stage. The first two have been widely used in ECG and heart rate variability analysis while the second derivative method aims to explain the success of the Hilbert transform. The algorithms were compared in terms of the number of false positive and false negative detections produced for records of the MIT/BIH Arrhythmia Database. The Hilbert transformer and the squaring function both produced a sensitivity and positive predictivity of over 99%, though the squaring function had a lower overall detection error rate. The second derivative resulted in the highest overall detection error rate. Different algorithms performed better for diverse ECG characteristics; suggesting that an algorithm can be specified for different recordings, the algorithms can be combined based on each one's characteristics to determine a new more accurate method, or an additional detection stage can be added to reduce the number of false negatives. Index Terms—Electrocardiography, Heart-rate variability, Hilbert transform, Peak detection

Quantitative Analysis of QRS Detection Algorithms Based on the First Derivative of the ECG

Obstructive sleep apnea syndrome (OSAS) is observed in approximately 2% of children. Heart rate vari- ability (HRV) is a potentially simple, non-invasive diagnostic screening tool for OSAS. In this study, we investigated the diagnostic potential of HRV using power spectral analysis, numerical titration, sample entropy, and detrended fluctuation analysis. Effects of sleep stages (REM and NREM sleep) are evaluated. The results show that the heart rate chaos intensity, as measured by the noise limit in numerical titration, is significantly higher during REM sleep than NREM sleep in all patient groups. By using the receiver-operating characteristic analysis, the detection of OSAS yielded a specificity of 72.2% and sensitivity of 81.3% using the numerical-titration technique. The findings suggest that sleep state and disordered breathing are important determinants of cardiac autonomic control. Nonlinear techniques such as numerical titration, when used in conjunction with spectral analysis of HRV could be an effective screening tool for pediatric OSAS.

Heart rate variability in pediatric obstructive sleep apnea.

Linear and nonlinear indices of heart rate variability (HRV) have been shown to predict mortality in congestive heart failure (CHF). However, most nonlinear indices describe only the fractality or complexity of HRV but not the intrinsic chaotic properties. In the present study, we performed linear (time- and frequency-domain), complexity (sample entropy), fractal (detrended fluctuation analysis) and chaos (numerical titration) analyses on the HRV of 50 CHF patients from the United Kingdom heart failure evaluation and assessment of risk trial database. Receiver operating characteristic and survival analysis yielded the chaos level to be the best predictor of mortality (followed by low/high frequency power ratio, LF/HF), such that these indices were significant in both univariate and multivariate models. These results indicate the power of heart rate chaos analysis as a potential prognostic tool for CHF.

Natalia M. Arzeno

Papers

Analysis of First-Derivative Based QRS Detection Algorithms

Chaotic Signatures of Heart Rate Variability and Its Power Spectrum in Health, Aging and Heart Failure

Quantitative Analysis of QRS Detection Algorithms Based on the First Derivative of the ECG

Heart rate variability in pediatric obstructive sleep apnea.

Heart Rate Chaos as a Mortality Predictor in Mild to Moderate Heart Failure