一种新型睡眠呼吸暂停诊治仪

介绍了一种基于胸腹运动检测法,应用压电薄膜式传感器的新型便携式睡眠呼吸暂停监测仪的原理和应用,为睡眠呼吸暂停征的普及检测提供了一种简易手段。     

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。     

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.     

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.     

AMS 6915压力传感器在睡眠健康中的应用

睡眠呼吸暂停是一种影响人类睡眠健康的常见疾病,然而许多患者通常并不知道他们患有此类疾病。如今,随着现代医疗技术的不断进步,此类疾病的诊断和治疗可以非常简单地在家中进行。高精度和小型化的压力测量技术可以在后台独立地进行工作,不仅可以帮助和改善受此影响的病人的生活,同时在降低医疗保健系统的成本方面有着很大的作用。文章叙述了有关睡眠呼吸暂停的疾病起因和监控办法,介绍了一种称为CPAP的呼吸机,通过智能化的压力和流量的测量和控制,可以用来诊断睡眠呼吸暂停疾病,同时也可以辅助病人呼吸。文章重点介绍了用于医用设备的小型OEM压力传感器——AMS 6915。     

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。     

睡眠呼吸暂停监测仪的研究

介绍了睡眠呼吸暂停监测的基本原理和重要意义,研究了成本低、体积小、耗电少、使用方便的睡眠呼吸暂停监测仪。     

基于PVDF压电薄膜的阻塞型呼吸暂停监测方法

介绍了一种基于压电信号的阻塞型睡眠呼吸暂停监测方法。聚偏氟乙烯(PVDF)压电薄膜可以采集到人躺在床上时由于胸腔振动而产生的压电信号。采集到的压电信号经过电荷放大和滤波之后由微控制器经过模/数(A/D)采样转换为数字信号。数字化的压电信号经过算法处理得出阻塞型呼吸暂停出现的次数以实现对阻塞型呼吸暂停的监测。经过初步的实验证明,提出的方法的监测准确率大于90%。基本满足睡眠呼吸暂停监测的需求。     

基于ARM的非接触式睡眠呼吸监测

为了进一步提高睡眠呼吸监测的便捷性,本文提出一种基于ARM的非接触式睡眠呼吸监测方法及系统。在现有的毫米波模块的基础上,通过频率、相位等信息计算出呼吸的频率。把呼吸心跳信号从毫米波回波信号中分离出来,再结合心率判断呼吸暂停。睡眠呼吸实验结果与多导睡眠监测仪进行比对,有较高的相关性。对非接触式呼吸监测有较好的应用价值。     

非入侵式睡眠呼吸监测系统的设计与实现

根据养老院、医院等特殊区域人群的睡眠呼吸监护需求,设计了非入侵式柔性压感睡眠呼吸监测系统。系统通过硬件电路设计,采集人体睡眠时的呼吸信号,并进行消噪、去趋势等预处理。在硬件终端中通过呼吸信号的幅度和周期的特征区分呼吸类别,并实时判断是否发生了呼吸暂停,记录暂停的时刻与持续时长,并将数据通过蓝牙传至手机,在手机APP上可绘制实时波形,手机把数据上传至云平台。PC端软件可从云平台获取数据,绘制拟合呼吸信号曲线,判定记录睡眠数据。经实验测试,系统判定呼吸次数与实际基本一致,并可准确判断呼吸暂停情况,满足长程实现睡眠呼吸监测的要求。     

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.     

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.     

Automated Sleep apnea detection using optimal duration-frequency concentrated wavelet-based features of pulse oximetry signals

Applied Intelligence - Sleep apnea is a potential sleep disorder, which deteriorates the quality of sleep. It is characterized by the obstruction in nasal airflow, which results in a low...     

Deep learning for sleep stages classification: modified rectified linear unit activation function and modified orthogonal weight initialisation

Multimedia Tools and Applications - Each stage of sleep can affect human health, and not getting enough sleep at any stage may lead to sleep disorder like parasomnia, apnea, insomnia, etc....     

Fuzzy reasoning used to detect apneic events in the sleep apnea-hypopnea syndrome

The sleep apnea/hypopnea syndrome is a very common sleep disorder, characterised by disrupted breathing during sleep. Depending on the extent of the disruptions to sleep, these are classified as apneas or hypopneas. In order to locate these apneic events an analysis of respiratory signals recorded for an entire night’s sleep is necessary. However, identifying and classifying apneic events is a complex task, given the error associated with the process for digitising signals, variability in expert criteria and the complexity of the signals themselves. This article describes a fuzzy-logic-based automated system for detecting apneic events and classifying them as apneas or hypopneas. The aim is to equip this system with mechanisms for dealing with imprecision and reasoning affected by uncertainty. The ultimate goal was to assist the physician in diagnosing the sleep apnea/hypopnea syndrome. Results in terms of locating events in the polysomnogram showed sensitivity and specificity of 0.87 and 0.89, respectively. A receiver operating curve index of 0.88 was obtained for the classification of events as apneas or hypopneas.     

基于X线的成人OSA计算机辅助诊断综述

阻塞性睡眠呼吸暂停（Obstructive Sleep Apnea,OSA）是成年人较为常见的呼吸类疾病之一,该疾病的特点是睡眠过程中频繁出现上气道完全或部分塌陷,严重影响人们的睡眠质量以及身体健康。阻塞性睡眠呼吸暂停综合征的诊断主要依靠多导睡眠监测,但这种方法无法满足目前大量的诊断需求。随着人工智能的出现及发展,假设深度学习可以有效地协助医生进行诊断该综合征。主要从阻塞性睡眠呼吸暂停的临床诊断方式出发,介绍了颅面侧位片作为诊断数据集的优势,以及人工智能诊断OSA的现状,提出了人工智能辅助医师诊断OSA的技术路线,分析了目前该诊断系统仍然存在的问题和挑战。     

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.