儿童阻塞性睡眠呼吸暂停计算机人脸辅助诊断综述

利用人脸图片辅助诊断儿童阻塞性睡眠呼吸暂停（OSA）可以减轻医生的负担,提高诊断的准确率。首先,简要阐述了目前儿童OSA临床诊断中的方法及其局限性;然后,在研究了目前已有方法的基础上,结合计算机人脸辅助诊断其他疾病的方法,将计算机人脸辅助诊断儿童OSA的方法分为三种类型：传统的计算机人脸辅助诊断方法、基于迁移学习的诊断方法、基于3D人脸数据的诊断方法,综述了三种类型的方法中的关键步骤,并对这些关键步骤中使用的方法进行了对比研究,为将来儿童OSA计算机人脸辅助诊断的研究提供了不同的切入点;最后,分析了儿童OSA诊断未来研究中的机遇和挑战。     

基于LabVIEW的睡眠呼吸暂停检测与远程监护

睡眠呼吸暂停综合症(SAS)是一种发病率极高的睡眠呼吸疾病,严重影响人的睡眠质量;针对目前SAS的检测尚未普及,研究了一种家用简易型的检测方法;通过实时采集被测者的呼吸信号,利用LabVIEW对信号进行分析处理判断发生睡眠呼吸暂停的情况,呼吸暂停每小时出现5次以上或7小时的睡眠中出现30次以上,即诊断为患有SAS,经实验证明该方法可实现对SAS的初步筛查;通过互联网,可使医护人员对被测者进行远程监护,该方法对SAS的预防和早期诊断具有重要价值.     

基于模糊PID的睡眠呼吸机控制系统设计

持续气道正压(Continuous Positive Airway Pressure,CPAP)通气是目前治疗阻塞性睡眠呼吸暂停(Obstructive sleep apnea,OSA)最为有效的方式之一。但在实际应用中,由于受到患者自主呼吸的影响,使得气道压力很难保持稳定。为了降低患者自主呼吸对设定压力的干扰,以及消除患者呼气时的憋闷感,模糊PID控制方法被应用于睡眠呼吸机CPAP的压力控制上。本文介绍了硬件系统结构并通过查询模糊规则表的方法实现了Fuzzy PID的算法,最后,使用了气体流量分析仪(VT PLUS HF)对治疗时的压力曲线进行了测试。结果表明,压力的波动性满足了睡眠呼吸暂停治疗设备的相关标准。     

DP801睡眠呼吸监测装置的制作

一、简介: 睡眠呼吸监测装置是一种对于病人生命安全的监护设备,主要用来对打鼾的人或新生儿在睡眠时出现呼吸困难甚至窒息现象进行全夜的长时间呼吸监测。通过监测病人出现呼吸暂停的程度,可以初步判断病人是否患有睡眠呼吸暂停症或呼吸障碍。当病人出现危及生命的窒息现象时,该装置还可以自动报警。这个监测装置主要由呼吸传感器、接口电路、DP801单片机三部分组成。     

用于呼吸暂停综合症的呼吸监测系统的研制

睡眠呼吸暂停综合征(Sleep Apnea Syndrome,SAS)是一种发病率高,具有潜在危险性的疾病.其分析诊断主要依赖于多参数自动监测仪,其中呼吸参数是重要的检测参数.本文研究了基于温感式检测法的检测原理,分析了传感器的动态特性,探讨了干扰产生的原因,提出了干扰的抑制方法,设计了相关的检测电路和数据采集电路,实现了呼吸参数的检测.     

一种检测睡眠呼吸暂停和阻塞的新方法

呼吸机对阻塞性睡眠呼吸暂停综合症OSAS(Obstructive Sleep Apnea Syndrome)的治疗安全有效,其与患者的顺应性是决定治疗效果和呼吸舒适性的关键因素。根据压差传感器采集到的呼吸信号波形的特点,提出一种实时检测睡眠呼吸暂停SA(Sleep Apnea)和判断阻塞的方法,其结果可用于自适应地调节呼吸机输出气流压力大小。将此方法同时应用于ResMed呼吸机与自行设计呼吸机进行对比实验,结果表明该方法实时有效,可以有效提高治疗的顺应性。     

远程睡眠呼吸暂停报警监护仪

本文主要介绍了一款能够定时开机,并具有睡眠呼吸暂停报警功能的能够监测睡眠呼吸紊乱疾病的诊断筛查设备,仪器具有:鼻气流/鼾声、血氧饱和度、脉搏数据实时监测功能,并据此生成诊断报告,供临床筛查或进一步研究使用。仪器使用时如果有呼吸暂停发生,则可通过蜂鸣器发出报警信号以提示旁人进行一定的处理或唤醒病人进行睡眠状态调整。实现了实时监控的目的,同时记录下病例,可通过PC机系统分析软件对病例进行自动分析,也可以发至远程网络终端由专家进行远程诊断,实现患者足不出户就能检测的目的。     

基于ARM的呼吸暂停综合征检测系统

阻塞型睡眠呼吸暂停综合征（Obstructive Sleep Apnea Hypopnea Syndrome, OSAHS）是一种常见的呼吸睡眠疾病，它会降低人们的睡眠质量，使人们产生疲惫感，更严重地会危害人们的身心健康。研究设计了一种基于ARM的OSAHS检测系统，系统以i.MX6ULL作为硬件主控，采用嵌入式Linux系统为软件平台，具有鼾声采集处理、检测分类、传输等功能，与云平台建立完整的OSAHS检测系统，并且通过与标准多导睡眠监测仪（PSG）设备对比检测效果达到83.9%，达到初筛的作用，具有较强的辅助诊断应用价值。     

腕表式睡眠呼吸暂停监测系统设计

设计了一种便携的腕表式睡眠呼吸暂停监测系统,避免了住院监测带来的高费用和低舒适度.以STM32为处理核心,通过控制传感器,实时监测用户的呼吸气流、血氧饱和度、心电图和胸腹运动情况,判断是否发生了呼吸暂停,记录并显示整晚呼吸暂停的总次数,同时将所测生理参数通过低功耗蓝牙发送到智能手机或平板应用程序,供医生进一步分析.将系统与标准多导睡眠仪测试结果对比,两者测得的呼吸暂停低通气指数(AHI)相关性和一致性较好.系统简便实用、测量结果可靠,扩大了睡眠呼吸暂停综合征的筛查人群.     

可穿戴式人体呼吸状态监测系统的设计

设计了一种基于蓝牙的可穿戴式睡眠呼吸暂停低通气综合征监测装置,通过该装置可以实时检测到睡眠呼吸暂停低通气综合征病人的睡眠呼吸状态。可穿戴技术实现基本生理信号的低负荷获取;蓝牙实现呼吸数据短距离无线传输且方便与PDA或Android智能手机等手持终端通信,保证了对病人的连续实时监测。     

Automated detection of cyclic alternating pattern and classification of sleep stages using deep neural network

The visual sleep stages scoring by human experts is the current gold standard for sleep analysis. However, this method is tedious, time-consuming, prone to human errors, and unable to detect microstructure of sleep such as cyclic alternating pattern (CAP) which is an important diagnostic factor for the detection of sleep disorders such as insomnia and obstructive sleep apnea (OSA). The CAP is only observed as subtle changes in the electroencephalogram (EEG) signals during non-rapid eye movement (NREM) sleep, making it very difficult for human experts to discern. Hence, it is important to have an automated system developed using artificial intelligence for accurate and robust detection of CAP and sleep stages classification. In this study, a deep learning model based on 1-dimensional convolutional neural network (1D-CNN) is proposed for CAP detection and homogenous 3-class sleep stages classification, namely wakefulness (W), rapid eye movement (REM) and NREM sleep. The proposed model is developed using standardized EEG recordings. Our developed CNN network achieved good model performance for 3-class sleep stages classification with a classification accuracy of 90.46%. Our proposed model also yielded a classification accuracy of 73.64% using balanced CAP dataset, and sensitivity of 92.06% with unbalanced CAP dataset. Our proposed model correctly identified majority of A-phases which comprised of only 12.6% in the unbalanced dataset. The performance of the developed prototype is ready to be tested with more data before clinical application.

     

A neural network method for detection of obstructive sleep apnea and narcolepsy based on pupil size and EEG.

Electroencephalogram (EEG) is able to indicate states of mental activity ranging from concentrated cognitive efforts to sleepiness. Such mental activity can be reflected by EEG energy. In particular, intrusion of EEG theta wave activity into the beta activity of active wakefulness has been interpreted as ensuing sleepiness. Pupil behavior can also provide information regarding alertness. This paper develops an innovative signal classification method that is capable of differentiating subjects with sleep disorders which cause excessive daytime sleepiness (EDS) from normal control subjects who do not have a sleep disorder based on EEG and pupil size. Subjects with sleep disorders include persons with untreated obstructive sleep apnea (OSA) and narcolepsy. The Yoss pupil staging rule is used to scale levels of wakefulness and at the same time theta energy ratios are calculated from the same 2-s sliding windows by Fourier or wavelet transforms. Then, an artificial neural network (NN) of modified adaptive resonance theory (ART2) is utilized to identify the two groups within a combined group of subjects including those with OSA and healthy controls. This grouping from the NN is then compared with the actual diagnostic classification of subjects as OSA or controls and is found to be 91% accurate in differentiating between the two groups. The same algorithm results in 90% correct differentiation between narcoleptic and control subjects.     

Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques

Obstructive sleep apnea is a syndrome which is characterized by the decrease in air flow or respiratory arrest depending on upper respiratory tract obstructions recurring during sleep and often observed with the decrease in the oxygen saturation. The aim of this study was to determine the connection between the respiratory arrests and the photoplethysmography (PPG) signal in obstructive sleep apnea patients. Determination of this connection is important for the suggestion of using a new signal in diagnosis of the disease. Thirty-four time-domain features were extracted from the PPG signal in the study. The relation between these features and respiratory arrests was statistically investigated. The Mann–Whitney U test was applied to reveal whether this relation was incidental or statistically significant, and 32 out of 34 features were found statistically significant. After this stage, the features of the PPG signal were classified with k-nearest neighbors classification algorithm, radial basis function neural network, probabilistic neural network, multilayer feedforward neural network (MLFFNN) and ensemble classification method. The output of the classifiers was considered as apnea and control (normal). When the classifier results were compared, the best performance was obtained with MLFFNN. Test accuracy rate is 97.07 % and kappa value is 0.93 for MLFFNN. It has been concluded with the results obtained that respiratory arrests can be recognized through the PPG signal and the PPG signal can be used for the diagnosis of OSA.

     

Detection of apneic events from single channel nasal airflow using 2nd derivative method

Detection of sleep apnea is one of the major tasks in sleep studies. Several methods, analyzing the various features of bio-signals, have been applied for automatic detection of sleep apnea, but it is still required to detect apneic events efficiently and robustly from a single nasal airflow signal under varying situations. This study introduces a new algorithm that analyzes the nasal airflow (NAF) for the detection of obstructive apneic events. It is based on mean magnitude of the second derivatives (MMSD) of NAF, which can detect respiration strength robustly under offset or baseline drift. Normal breathing epochs are extracted automatically by examining the stability of SaO(2) and NAF regularity for each subject. The standard MMSD and period of NAF, which are regarded as the values at the normal respiration level, are determined from the normal breathing epochs. In this study, 24 Polysomnography (PSG) recordings diagnosed as obstructive sleep apnea (OSA) syndrome were analyzed. By analyzing the mean performance of the algorithm in a training set consisting of three PSG recordings, apnea threshold is determined to be 13% of the normal MMSD of NAF. NAF signal was divided into 1-s segments for analysis. Each segment is compared with the apnea threshold and classified into apnea events if the segment is included in a group of apnea segments and the group satisfies the time limitation. The suggested algorithm was applied to a test set consisting of the other 21 PSG recordings. Performance of the algorithm was evaluated by comparing the results with the sleep specialist's manual scoring on the same record. The overall agreement rate between the two was 92.0% (kappa=0.78). Considering its simplicity and lower computational load, the suggested algorithm is found to be robust and useful. It is expected to assist sleep specialists to read PSG more quickly and will be useful for ambulatory monitoring of apneas using airflow signals.     

An expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder. The traditional diagnosis methods of the disorder are cumbersome and expensive. The ability to automatically identify OSA from electrocardiogram (ECG) recordings is important for clinical diagnosis and treatment. In this study, we proposed an expert system based on discrete wavelet transform (DWT), fast-Fourier transform (FFT) and least squares support vector machine (LS-SVM) for the automatic recognition of patients with OSA from nocturnal ECG recordings. Thirty ECG recordings collected from normal subjects and subjects with sleep apnea, each of approximately 8 h in duration, were used throughout the study. The proposed OSA recognition system comprises three stages. In the first stage, an algorithm based on DWT was used to analyze ECG recordings for the detection of heart rate variability (HRV) and ECG-derived respiration (EDR) changes. In the second stage, an FFT based power spectral density (PSD) method was used for feature extraction from HRV and EDR changes. Then, a hill-climbing feature selection algorithm was used to identify the best features that improve classification performance. In the third stage, the obtained features were used as input patterns of the LS-SVM classifier. Using the cross-validation method, the accuracy of the developed system was found to be 100% for using a subset of selected combination of HRV and EDR features. The results confirmed that the proposed expert system has potential for recognition of patients with suspected OSA by using ECG recordings.     

Automated sleep stage scoring using hybrid rule- and case-based reasoning.

We propose an automated method for sleep stage scoring using hybrid rule- and case-based reasoning. The system first performs rule-based sleep stage scoring, according to the Rechtschaffen and Kale's sleep-scoring rule (1968), and then supplements the scoring with case-based reasoning. This method comprises signal processing unit, rule-based scoring unit, and case-based scoring unit. We applied this methodology to three recordings of normal sleep and three recordings of obstructive sleep apnea (OSA). Average agreement rate in normal recordings was 87.5% and case-based scoring enhanced the agreement rate by 5.6%. This architecture showed several advantages over the other analytical approaches in sleep scoring: high performance on sleep disordered recordings, the explanation facility, and the learning ability. The results suggest that combination of rule-based reasoning and case-based reasoning is promising for an automated sleep scoring and it is also considered to be a good model of the cognitive scoring process.     

Pre-determination of OSA degree using morphological features of the ECG signal

Obstructive sleep apnea (OSA) is a very common, but a difficult sleep disorder to diagnose. Recurrent obstructions form in the airway during sleep, such that OSA can threaten a breathing capacity of patients. Clinically, continuous positive airway pressure (CPAP) is the most specific and effective treatment for this. In addition, these patients must be separated according to its degree, with CPAP treatment applied as a result. In this study, 30 OSA patients from two different databases were automatically classified using electrocardiogram (ECG) data, identified as mild, moderate, and severe. One of the databases was original recordings which had 9 OSA patients with 8303 epochs and the other one was Physionet benchmark database which had 21 patients with 20,824 epochs. Fifteen morphological features could be identified when apnea was seen, both before and after it presented. Five data groups in total for first dataset and second dataset were prepared with these features and 10-fold cross validation was used to effectively determine the test data. Then, sequential backward feature selection (SBFS) algorithm was applied to understand the more effective features. The prepared data groups were evaluated with artificial neural networks (ANN) to obtain optimum classification performance. All processes were repeated for ten times and error deviation was calculated for the accuracy. Furthermore, different classifiers which are frequently used in the literature were tested with selected features. The degree of OSA was estimated from three epochs in pre-apnea data, yielding the success rates of 97.20 ± 2.15% and 90.18 ± 8.11% with the SBFS algorithm for the first and second datasets, respectively. Also, SVM classifier followed ANN system in the success rates of 96.23 ± 3.48% and 88.75 ± 8.52% for used datasets.     

An automatic apnea screening algorithm for children

Sleep Disordered Breathing (SDB) is a group of diseases that affect the normal respiratory function during sleep, from primary snoring to obstructive sleep apnea (OSA) being the most severe. SDB can be detected using a complex and expensive exam called polysomnography. This exam monitors the sleep of a person during the night by measuring 21 different signals from an Electrocardiogram to Nasal Air Flow. Several automatic methods have been developed to detect this disorder in adults, with a very high performance and using only one signal. However, we have not found similar algorithms especially developed for Children. We benchmarked 6 different methods developed for adults. We showed empirically that those models’ performance is drastically reduced when used on children (under 15 years old). Afterwards, we present a new approach for screening children with risk of having SDB. Moreover, our algorithm uses less information than a polysomnography and out performs state-of-the-art techniques when used on children. We also showed empirically that no signal alone is a good SDB screening in children. Moreover, we discover that combinations of three signals which are not used in any other previous work are the best for this task in children.     

Detection and classification of sleep apnea using genetic algorithms and SVM‐based classification of thoracic respiratory effort and oximetric signal features

Sleep apnea is a relatively prevalent breathing disorder characterized by temporary interruptions in airflow during sleep. There are 2 major types of sleep apnea. Obstructive sleep apnea occurs when air cannot flow through the upper airway despite efforts to breathe. Central sleep apnea occurs when the brain fails to signal to the muscles to maintain breathing. The standard diagnostic test is polysomnography, which is expensive and time consuming. The aim of this study was to design an automatic diagnostic and classifying algorithm for sleep apneas employing thoracic respiratory effort and oximetric signals. This algorithm was trained and tested applying a database of 54 subjects who had undergone polysomnography. A feature extraction stage was conducted to compute features. An optimal genetic algorithm was applied to select optimal features of these 2 kinds of signals. The classification technique was based on the support vector machine classifier to classify the selected features in 3 classes as healthy, obstructive, and central sleep apnea events. The results show that our automated classification algorithm can diagnose sleep apnea and its types with an average accuracy level of 90.2% (87.5‐95.8) in the test set and 90.9% in the validation set with high acceptable accuracy.