Cardiorespiratory-based sleep staging in subjects with obstructive sleep apnea

A cardiorespiratory-based automatic sleep staging system for subjects with sleep-disordered breathing is described. A simplified three-state system is used: Wakefulness (W), rapid eye movement (REM) sleep (R), and non-REM sleep (S). The system scores the sleep stages in standard 30-s epochs. A number of features associated with the epoch RR-intervals, an inductance plethysmography estimate of rib cage respiratory effort, and an electrocardiogram-derived respiration (EDR) signal were investigated. A subject-specific quadratic discriminant classifier was trained, randomly choosing 20% of the subject's epochs (in appropriate proportions of W, S and R) as the training data. The remaining 80% of epochs were presented to the classifier for testing. An estimated classification accuracy of 79% (Cohen's kappa value of 0.56) was achieved. When a similar subject-independent classifier was trained, using epochs from all other subjects as the training data, a drop in classification accuracy to 67% (kappa = 0.32) was observed. The subjects were further broken in groups of low apnoea-hypopnea index (AHI) and high AHI and the experiments repeated. The subject-specific classifier performed better on subjects with low AHI than high AHI; the performance of the subject-independent classifier is not correlated with AHI. For comparison an electroencephalograms (EEGs)-based classifier was trained utilizing several standard EEG features. The subject-specific classifier yielded an accuracy of 87% (kappa = 0.75), and an accuracy of 84% (kappa = 0.68) was obtained for the subject-independent classifier, indicating that EEG features are quite robust across subjects. We conclude that the cardiorespiratory signals provide moderate sleep-staging accuracy, however, features exhibit significant subject dependence which presents potential limits to the use of these signals in a general subject-independent sleep staging system.     

Automatic detection of obstructive sleep apnea using speech signals

Obstructive sleep apnea (OSA) is a common disorder associated with anatomical abnormalities of the upper airways that affects 5% of the population. Acoustic parameters may be influenced by the vocal tract structure and soft tissue properties. We hypothesize that speech signal properties of OSA patients will be different than those of control subjects not having OSA. Using speech signal processing techniques, we explored acoustic speech features of 93 subjects who were recorded using a text-dependent speech protocol and a digital audio recorder immediately prior to polysomnography study. Following analysis of the study, subjects were divided into OSA (n=67) and non-OSA (n=26) groups. A Gaussian mixture model-based system was developed to model and classify between the groups; discriminative features such as vocal tract length and linear prediction coefficients were selected using feature selection technique. Specificity and sensitivity of 83% and 79% were achieved for the male OSA and 86% and 84% for the female OSA patients, respectively. We conclude that acoustic features from speech signals during wakefulness can detect OSA patients with good specificity and sensitivity. Such a system can be used as a basis for future development of a tool for OSA screening.     

Percutaneous biphasic electrical stimulation for treatment of obstructive sleep apnea syndrome

In this paper, we study the effect of stimulation of the genioglossus with percutaneous biphasic electrical pulses on patients with the obstructive sleep apnea syndrome (OSAS). The experiment was conducted in 22 patients clinically diagnosed with OSAS. The patients were monitored with polysomnography (PSG) in the trial. When the sleep apnea was detected, the genioglossus was stimulated with percutaneous biphasic electrical pulses that were automatically regulated by a microcontroller to achieve the optimal effect. The percutaneous biphasic electrical stimulation caused contraction of the genioglossus, forward movement of the tongue, and relieving of the glossopharyngeal airway obstruction. The SaO2, apnea time, hypoxemia time, and change of respiratory disturbance index (RDI) were compared in patients with treatment and without treatment. With percutaneous biphasic electrical stimulation of the genioglossus, the OSAS patients showed apnea time decreased (P < 0.01), RDI decreased (P < 0.01), and SaO2 increased (P < 0.01). No tissue injury or major discomfort was noticed during the trial. The stimulation of genioglossus with percutaneous biphasic electrical current pulse is an effective method for treating OSAS.     

Automated recognition of obstructive sleep apnea syndrome using support vector machine classifier

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.     

老年男性阻塞性睡眠呼吸暂停程度与代谢综合征的相关性研究

目的：研究老年男性阻塞性睡眠呼吸暂停综合征（obstructive sleep apnea syndrome ,OSAS）严重程度与代谢综合征的相关性。方法：随机选择我院收治的663例老年患者,根据呼吸暂停及低通气指数,将其分为非OSAS 组和轻、中、重度OSAS 组。比较各组患者颈围、体质指数（BMI）、腰臀比、血压、血脂、空腹血糖等,分析OSAS严重程度与代谢综合征及其组分发病率之间的相关性。结果 OSAS患者的颈围、BMI、腰臀比、收缩压、舒张压、空腹血糖等显著高于非OSAS组患者。OSAS严重程度与颈围、BMI、甘油三酯等存在显著正相关,且随着OSAS严重程度的增加,代谢综合征及其组分的发生率呈逐渐递增趋势。结论：老年男性阻塞性睡眠呼吸暂停程度与代谢综合征发病率密切相关,这可能是阻塞性睡眠呼吸暂停综合征增加心血管疾病发生率和死亡率的主要原因。     

Use of sample entropy approach to study heart rate variability in obstructive sleep apnea syndrome

Sample entropy, a nonlinear signal processing approach, was used as a measure of signal complexity to evaluate the cyclic behavior of heart rate variability (HRV) in obstructive sleep apnea syndrome (OSAS). In a group of 10 normal and 25 OSA subjects, the sample entropy measure showed that normal subjects have significantly more complex HRV pattern than the OSA subjects (p < 0.005). When compared with spectral analysis in a minute-by-minute classification, sample entropy had an accuracy of 70.3% (69.5% sensitivity, 70.8% specificity) while the spectral analysis had an accuracy of 70.4% (71.3% sensitivity, 69.9% specificity). The combination of the two methods improved the accuracy to 72.9% (72.2% sensitivity, 73.3% specificity). The sample entropy approach does not show major improvement over the existing methods. In fact, its accuracy in detecting sleep apnea is relatively low in the well classified data of the physionet. Its main achievement however, is the simplicity of computation. Sample entropy and other nonlinear methods might be useful tools to detect apnea episodes during sleep.     

改良小儿腭咽成型术治疗小儿睡眠呼吸暂停综合征疗效观察

目的:探讨改良小儿腭咽成型术用于治疗小儿OSAS的效果。方法:将195例扁桃体切除术患者随机分为治疗组108例,对照组87例,两组病例均行常规的扁桃体切除术,术后治疗组病例将腭舌弓、腭咽弓及扁桃体窝组织一并缝合,修复创面,封闭创腔。对照组病例经压迫止血或双极电凝止血成功,术创未予缝合。对比研究治疗组与对照组术后症状缓解、出血并发症及患儿术后不适反映的情况。结果:术后症状缓解:治疗组有效率96.30%,对照组有效率85.06%。治疗组病例无1例发生术后原发性或继发性出血。对照组病例发生原发性出血1例,继发性出血3例,出血并发症的发生率为4.56%。术后观察患儿哭闹情况及进食情况,治疗组和对照组无明显差别。结论:改良小儿腭咽成型术对治疗小儿OSAS疗效可靠,安全性高,小儿舒适度好。     

低温等离子消融术治疗阻塞性睡眠呼吸暂停低通气综合征的护理

目的：总结低温等离子消融术治疗阻塞性睡眠呼吸暂停低通气综合征的护理。方法：回顾性分析我院收治的36例经低温等离子消融术治疗的阻塞性睡眠呼吸暂停低通气综合征病例。结论：低温等离子消融术治疗阻塞性睡眠呼吸暂停低通气综合征是一种安全、有效的方法。术前及术后的有效护理是手术成功的重要环节。     

Respiration-movement-based sleep apnea monitor

An electronics hardware-based monitor is presented for the detection and differentiation of all forms of sleep apnea solely by the information from abdominal and thoracic excursion signals. The working of the developed monitor has been successfully tested by generating the conditions of various forms of apnea artificially.     

Utility of approximate entropy from overnight pulse oximetry data in the diagnosis of the obstructive sleep apnea syndrome

Approximate entropy (ApEn) is a family of statistics introduced as a quantification of regularity in time series without any a priori knowledge about the system generating them. The aim of this preliminary study was to assess whether a time series analysis of arterial oxygen saturation (SaO2) signals from overnight pulse oximetry by means of ApEn could yield essential information on the diagnosis of obstructive sleep apnea (OSA) syndrome. We analyzed SaO2 signals from 187 subjects: 111 with a positive diagnosis of OSA and 76 with a negative diagnosis of OSA. We divided our data in a training set (44 patients with OSA Positive and 30 patients with OSA Negative) and a test set (67 patients with OSA Positive and 46 patients with OSA Negative). The training set was used for algorithm development and optimum threshold selection. Results showed that recurrence of apnea events in patients with OSA determined a significant increase in ApEn values. This method was assessed prospectively using the test dataset, where we obtained 82.09% sensitivity and 86.96% specificity. We conclude that ApEn analysis of SaO2 from pulse oximetric recording could be useful in the study of OSA.     

超脉冲CO2激光悬雍垂腭咽成形术治疗鼾症的近期临床研究

目的：探讨超脉冲CO2激光悬雍垂腭咽成形术治疗鼾症的可行性，技术操作及疗效。方法：采用超脉冲CO2激光悬雍垂腭咽成型术治疗鼾症64例。结果：该组病例近期均获得满意疗效，远期疗效达87.5%。术中出血极少，勿需缝合切口，切割创缘周期组织反应轻，手术时间短，无并发症发生。结论：该术式具有明显的临床应用价值，值得推广应用。     

Toward prediction of physiological state signals in sleep apnea

A recurrent connectionist model is described to predict dynamic respiratory state in the apneic sleeping patient. The time-domain model of nonlinear time-lagged interactions between heart rate, respiration, and oxygen saturation was developed to implicitly embed the dynamics of the respiration and cardiovascular control systems. Multiple future time scales were enforced on the network during training to explore the limits of the prediction horizon and produce a global representation of dynamic state trajectory. Predicted apneic respiration state results are presented in terms of invariant geometric statistics (largest Lyapunov exponent λ_L and correlation dimension D_c). The λ_L prediction error was 13%, while D_c error was within 9% of the true time series value. The magnitude of these errors may fall within experimental noise levels. This methodology may eventually be useful in dynamic control of continuous positive airway pressure (CPAP) therapy devices, and may lead to increased patient compliance with this therapy     

软腭低温等离子消融术治疗老年阻塞性睡眠呼吸暂停低通气综合征

目的:探讨软聘低温等离子体消融术对老年阻塞性睡眠呼吸暂停低通气综合征(OSAHS)患者临床疗效。方法:选择27例老年OSAHS患者,分别于术前和术后2月行多导睡眠图等检查,比较悬雍垂长度、鼾声评级、软腭长度及Epworth嗜睡程度评分,观察术后疗效。结果:患者术前和术后2月PSG监测显示:术后I Ⅱ期睡眠/总睡眠时间(TST)(%)明显缩短,Ⅲ Ⅳ期睡眠/TST(%)明显延长、睡眠效率(TST/总记录时间%)及最低血氧饱和度(LSp02)均显著提高(P<0.05):此外,呼吸暂停低通气指数(AHI)降低较术前有显著意义(P<0.01);患者术后2月软腭长度、悬雍垂长度及鼾声评级的降低均较术前有显著意义(P<0.01):Epworth嗜睡程度评分亦较术前明显降低(P<0.01);27例患者术后1-3天对疼痛、讲话及吞咽的影响在轻度以内,术后1周进一步改善。结论:软腭低温等离子消融术对于老年OSAHS患者具有较明显的疗效,手术安全,无明显不良反应,适用于老年OSAHS患者。     

Real-time sleep apnea detection by classifier combination

To find an efficient and valid alternative of polysomnography (PSG), this paper investigates real-time sleep apnea and hypopnea syndrome (SAHS) detection based on electrocardiograph (ECG) and saturation of peripheral oxygen (SpO(2)) signals, individually and in combination. We include ten machine-learning algorithms in our classification experiment. It is shown that our proposed SpO (2) features outperform the ECG features in terms of diagnostic ability. More importantly, we propose classifier combination to further enhance the classification performance by harnessing the complementary information provided by individual classifiers. With our selected SpO(2) and ECG features, the classifier combination using AdaBoost with Decision Stump, Bagging with REPTree, and either kNN or Decision Table achieves sensitivity, specificity, and accuracy all around 82% for a minute-based real-time SAHS detection over 25 sleep-disordered-breathing suspects' full overnight recordings.     

一种新型睡眠呼吸暂停检测系统的设计

为了监测睡眠呼吸暂停低通气综合征,基于光栅编码传感器,设计了可穿戴式的呼吸暂停检测仪。光栅编码传感器被嵌入在一条系在患者腰部的腹带内,在呼吸过程中,由于腹部体积的变化引起腹带中光栅的位置发生变化。通过光电二极管传感器阵列对光栅的运动进行检测,采用单片机对信号进行分析处理,从而判断呼气和吸气的过程,计算出呼吸周期,若周期超时则为呼吸暂停。经多次实验,并与呼吸检测设备的检测结果进行比较,本系统的平均精确度高于95%。     

Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea

Sleep has been regarded as a testing situation for the autonomic nervous system, because its activity is modulated by sleep stages. Sleep-related breathing disorders also influence the autonomic nervous system and can cause heart rate changes known as cyclical variation. We investigated the effect of sleep stages and sleep apnea on autonomic activity by analyzing heart rate variability (HRV). Since spectral analysis is suited for the identification of cyclical variations and detrended fluctuation analysis can analyze the scaling behavior and detect long-range correlations, we compared the results of both complementary techniques in 14 healthy subjects, 33 patients with moderate, and 31 patients with severe sleep apnea. The spectral parameters VLF, LF, HF, and LF/HF confirmed increasing parasympathetic activity from wakefulness and REM over light sleep to deep sleep, which is reduced in patients with sleep apnea. Discriminance analysis was used on a person and sleep stage basis to determine the best method for the separation of sleep stages and sleep apnea severity. Using spectral parameters 69.7% of the apnea severity assignments and 54.6% of the sleep stage assignments were correct, while using scaling analysis these numbers increased to 74.4% and 85.0%, respectively. We conclude that changes in HRV are better quantified by scaling analysis than by spectral analysis.     

Autonomic cardiovascular control following transient arousal from sleep: a time-varying closed-loop model

Recent studies suggest that exposure to repetitive episodes of hypoxia and transient arousal can lead to increased risk for cardiovascular disease in patients with obstructive sleep apnea syndrome (OSAS). To obtain an improved understanding of and to quantitatively characterize the autonomic effects of arousal from sleep, a time-varying closed-loop model was used to determine the interrelationships among respiration, heart rate and blood pressure in 8 normal adults. A recursive least squares algorithm was used in combination with the Laguerre expansion technique to estimate the time-varying impulse responses of the 4 model components. We found that during arousal: 1) respiratory-cardiac coupling gain increases in nonrapid-eye movement (NREM) but not in REM sleep; 2) in both NREM and REM sleep, baroreflex gain shows an initial increase, but this is followed by a more sustained decrease below pre-arousal baseline levels, allowing sympathetic tone to be elevated over a relatively long duration; 3) the gains of other model components show increases with arousal that are consistent with the increased sympathetic modulation of systemic vascular resistance and contractility of the heart. These findings establish a normative database against which further measurements of cardiovascular arousal responses in OSAS may be compared.     

睡眠呼吸暂停综合征合并高血压与胰岛素抵抗的研究

目的观察OSAS合并高血压患者体内炎症水平和胰岛素抵抗的关系。方法用PSG检查确诊重度OSAS患者70例,其中40例合并高血压病。用全自动生化仪检测血清真胰岛素(FINS)、C肽、C反应蛋白、空腹血糖,用稳态模型(HOMA)计算IR指数和胰岛β细胞功能指数。结果OSAS合并高血压患者血清CRP、FINS、血清C肽、HOMA-IR和HOMA-islet均较单纯OSAS患者明显升高。结论OSAS患者的慢性炎症通过形成胰岛素抵抗,在OSAS合并高血压的过程中可能起重要作用。     

悬雍垂腭咽成形术联合鼻部手术治疗OSAHS疗效分析

目的探讨对合并鼻部和口咽部阻塞的阻塞性睡眠呼吸暂停低通气综合征（Obstructive Sleep Apnea—Hypopnea Syndrome,OSAHS）患者进行同期鼻部手术和悬雍垂腭咽成形术（uvulopalatopharyngoplasty,UPPP）的临床疗效。方法诊断和评估根据多导睡眠呼吸监测（Polysomonography,PSG）、体重指数（Body Mass Index,BMI）和主观症状。检查证实同时有口咽狭窄和鼻部异常,在行UPPP手术同期行下鼻甲部分切除术和（或）鼻中隔偏曲黏膜下切除术）,术后随访1-2年。结果截止到2007年11月,所有患者术后满12个月,复查时打鼾、憋气、头痛及嗜睡等症状均明显减轻或消失。结论UPPP联合鼻中隔偏曲矫正、下鼻甲部分切除术治疗OSAHS可取得良好的手术疗效。     

Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea

A method for the automatic processing of the electrocardiogram (ECG) for the detection of obstructive apnoea is presented. The method screens nighttime single-lead ECG recordings for the presence of major sleep apnoea and provides a minute-by-minute analysis of disordered breathing. A large independently validated database of 70 ECG recordings acquired from normal subjects and subjects with obstructive and mixed sleep apnoea, each of approximately eight hours in duration, was used throughout the study. Thirty-five of these recordings were used for training and 35 retained for independent testing. A wide variety of features based on heartbeat intervals and an ECG-derived respiratory signal were considered. Classifiers based on linear and quadratic discriminants were compared. Feature selection and regularization of classifier parameters were used to optimize classifier performance. Results show that the normal recordings could be separated from the apnoea recordings with a 100% success rate and a minute-by-minute classification accuracy of over 90% is achievable.