基于脑电小波分解的呼吸暂停自动检测方法

睡眠呼吸暂停是一种常见的睡眠呼吸紊乱,目前呼吸暂停的诊断主要依靠多导睡眠监测,但因其操作复杂、价格昂贵,且对使用环境要求较高,而难以实现家用普及。为此提出一种基于脑电信号小波分解的呼吸暂停自动检测方法。首先,对脑电信号进行4层小波分解,提取第2~4层细节系数;其次,在得到的细节系数绝对值中提取能量和方差两种特征;最后,建立k-近邻,支持向量机和随机森林等机器学习模型对特征进行分类。使用来自天津市胸科医院睡眠监测实验室30名受试的3 248个正常呼吸和呼吸暂停期间的脑电信号片段进行检测,结果显示,对呼吸暂停识别准确率、灵敏度、特异度分别达到93.85%、91.46%、96.27%,表明该方法可以实现呼吸暂停事件的高精度检测,有望用于呼吸暂停自动识别系统的设计,辅助医师进行呼吸暂停自动检测。     

基于子时段呼吸暂停和睡眠阶段的脑网络分析与分类

睡眠分期是评估睡眠质量的基础。然而,睡眠呼吸暂停(sleep apnea, SA)会改变测试者的睡眠结构,进而影响对睡眠分期的准确评估。因此,在评估睡眠质量时,准确检测睡眠呼吸暂停和睡眠分期至关重要。为准确评估睡眠分期,本研究通过研究脑区之间的功能连接,探讨了脑功能连接的相互作用关系。采用锁相值(phase locking value, PLV)在不同时间段上进行特征提取,构建功能连接网络;然后利用多个时间段的PLV进行特征融合,并通过LibSVM(library for support vector machines, LibSVM)结合分类性能优化策略的方法进行睡眠分期。同时,本研究还分析了睡眠呼吸暂停和正常呼吸对脑网络的影响。实验结果显示,睡眠呼吸暂停时的各脑区连通紧密程度大于正常呼吸时,并在子时段数为30时,睡眠分期的分类准确率达到了88.87%,呼吸暂停的检测准确率达到了93.64%。该算法在睡眠分类和呼吸暂停检测方面表现出良好性能,有助于推动脑电睡眠分类和呼吸暂停检测系统的开发和应用。     

用于睡眠呼吸暂停检测的心电特征稳定性分析

基于心电信号(electrocardiogram,ECG)的睡眠呼吸暂停检测具有十分重要的意义,很多研究致力于提高检测的准确率却忽视了特征的稳定性。本研究对用于睡眠呼吸暂停检测的心电特征进行稳定性分析,并建立呼吸暂停事件检测模型。基于集成稳定特征选择策略,将最小冗余最大相关(minimal-redundancy-maximal-relevance,mRMR)特征选择方法与稳健排序聚合(robust rank aggregation,RRA)方法结合,对45个心电特征进行稳定性分析。使用10折交叉验证及支持向量机(SVM)进行特征验证及检测模型建立。最终使用14个特征建立分类模型,在独立测试集上实现Acc=90.03%,Se=86.71%,Sp=91.73%,所选特征在稳定性及检测准确率方面有明显提高。     

基于24小时动态血压数据识别阻塞性睡眠呼吸暂停综合征研究EI北大核心CSCD

睡眠呼吸暂停会导致患者心跳呼吸骤停、睡眠节律紊乱、夜间低血氧和血压异常波动,最终导致高血压患者夜间靶器官损害。阻塞性睡眠呼吸暂停低通气综合征(OSAHS)发病率极高,严重影响了患者的身心健康。本研究尝试从24小时动态血压数据中提取与OSAHS相关的特征,通过机器学习模型识别OSAHS,从而实现该疾病的鉴别诊断。研究数据来自2018年12月至2019年12月解放军总医院睡眠门诊收集的339例患者的动态血压检查数据,其中经多导睡眠监测(PSG)确诊的OSAHS患者115例,非OSAHS患者224例。根据OSAHS患者血压临床变化特点,定义了特征提取规则并开发算法提取特征,而后使用logistic回归、lightGBM等模型对疾病进行分类预测。结果表明本研究所训练的lightGBM模型的识别准确率为80.0%,精确率为82.9%,召回率为72.5%,受试者工作特征曲线下面积(AUC)为0.906,所定义的动态血压特征能够有效用于OSAHS检测。本研究为OSAHS筛查提供了一种新的思路和方法。     

基于多通道的睡眠呼吸暂停检测

目的 为了提高检测性能和验证不同生理信号对睡眠呼吸暂停的检测结果,本文提出一种信号叠加和通道相加检测睡眠呼吸暂停的方法。方法 首先对100例睡眠呼吸障碍患者的心电(electrocardiogram, ECG)和脑电(electroencephalogram, EEG)信号通过小波阈值方法进行预处理,其次进行通道相加和信号叠加,然后通过Relief特征选择算法对30个特征进行分析,最后采用支持向量机(support vector machine, SVM)构建睡眠呼吸暂停分类模型,并验证该模型的准确性。结果 实验结果表明,通道相加和信号叠加时睡眠呼吸暂停检测的最高准确率分别为96.24%和96.18%。结论 ECG和EEG两种信号叠加和通道相加的方法均可提高睡眠呼吸暂停检测结果,且X2(ECG)和C3-A2(EEG)通道相加检测结果最好。     

下颌前移矫治器治疗阻塞性睡眠呼吸暂停对颏舌肌线粒体超微结构的影响

背景:目前阻塞性睡眠呼吸暂停的常用治疗方法为下颌前移矫治器,学者对颏舌肌功能异常可以引发阻塞性睡眠呼吸暂停发病已经达成共识,但对于引起颏舌肌损伤和功能障碍的机制以及相关治疗方法对颏舌肌的影响关注不足.目的:探讨下颌前移矫治器治疗阻塞性睡眠呼吸暂停对颏舌肌线粒体超微结构的保护作用,分析阻塞性睡眠呼吸暂停致颏舌肌结构功能紊...     

阻塞性睡眠呼吸暂停综合征对脑血管病患者预后的影响

目的探讨阻塞性睡眠呼吸暂停综合征对脑血管病患者预后的影响。方法30例合并阻塞性睡眠呼吸暂停综合征的患者作为研究组，30例无阻塞性睡眠呼吸暂停综合征的患者作为对照组．用临床神经功能缺失评分观察预后．结果研究组与对照组神经功能缺失评分有显著差异（P〈0．01）．结论阻塞性睡眠呼吸暂停综合征可影响患者的预后。可以做为影响脑血管病预后不良的独立指标。     

精神分裂症、肥胖症与阻塞性睡眠呼吸暂停

目的　本文对精神患者发生阻塞性睡眠呼吸暂停的危险因素进行了评估。方法　研究对象为某精神病医院的 36 4名进行睡眠障碍咨询的住院患者 ,其中 78%的患者进行了睡眠多导图检查。对不同诊断的患者 (按照 DSM- - R诊断标准 )发生阻塞性睡眠呼吸暂停的比例进行回顾性评估。结果　 L ogistic回归显示年龄 (P=.0 46 )、性别 (P=.0 0 2 )、体型指数 (P<.0 0 1)和长期服用精神安定剂 (P=.0 12 )均是发生阻塞性睡眠呼吸暂停的独立影响因素 ,其诊断标准为睡眠中 6 h内出现呼吸暂停和 (或 )呼吸不足多于 2 0次。与其它类型精神病相比 ,精神分裂…     

骨折延迟愈合及骨不连与阻塞性睡眠呼吸暂停的相关性

背景：阻塞性睡眠呼吸暂停能影响成骨和/或破骨细胞及骨愈合相关调节蛋白,使骨折患者不易愈合或加重不愈合的程度。 目的：观察阻塞性睡眠呼吸暂停与骨不连的相关性。 方法：随机选取338例骨折的患者,其中骨愈合患者228例,骨不连110例,经便携式睡眠仪筛查出合并阻塞性睡眠呼吸暂停的骨不连患者11例,分析监测数据与临床资料的相关性。 结果与结论：与骨愈合患者相比,骨不连患者阻塞性睡眠呼吸暂停患病率较高(P < 0.05),比值比(OR)=2.422,95%CI=0.996~5.891,合并阻塞性睡眠呼吸暂停的骨不连患者愈合时间与最低血氧饱和度、平均血氧饱和度呈负相关(r分别为0.40和0.38,P < 0.05),骨痂X射线评分与最低血氧饱和度、平均血氧饱和度呈正相关(r分别为0.34和0.47,P < 0.05)。提示阻塞性睡眠呼吸暂停可能是骨不连新的危险因素之一。     

低温等离子治疗儿童阻塞性睡眠呼吸暂停综合征围手术期护理

目的探讨儿童全麻低温等离子治疗阻塞性睡眠呼吸暂停综合征的围手术期护理。方法对45例阻塞性睡眠呼吸暂停综合症儿童施行全麻低温等离子治疗术前护理,术后并发症的观察及护理。结果45例患儿无1例发生并发症,治疗效果满意。结论全麻低温等离子治疗阻塞性睡眠呼吸暂停综合征是一种创伤小、出血少、疼痛轻、愈合好的一种手术方法,良好的围手术期护理配合是手术成功的保障。     

Detection of obstructive sleep apnea in pediatric subjects using surface lead electrocardiogram features

STUDY OBJECTIVES: To investigate the feasibility of detecting obstructive sleep apnea (OSA) in children using an automated classification system based on analysis of overnight electrocardiogram (ECG) recordings. DESIGN: Retrospective observational study. SETTING: A pediatric sleep clinic. PARTICIPANTS: Fifty children underwent full overnight polysomnography. INTERVENTION: N/A. MEASUREMENTS AND RESULTS: Expert polysomnography scoring was performed. The datasets were divided into a training set of 25 subjects (11 normal, 14 with OSA) and a withheld test set of 25 subjects (11 normal, 14 with OSA). Features, calculated from the ECG of the 25 training datasets, were empirically chosen to train a modified quadratic discriminant analysis classification system. The selected configuration used a segment length of 60 seconds and processed mean, SD, power spectral density, and serial correlation measures to classify segments as apneic or normal. By combining per-segment classifications and using receiver-operator characteristic analysis, a per-subject classifier was obtained that had a sensitivity of 85.7%, specificity of 90.9%, and accuracy of 88% on the training datasets. The same decision threshold was applied to the withheld datasets and yielded a sensitivity of 85.7%, specificity of 81.8%, and accuracy of 84%. The positive and negative predictive values were 85.7% and 81.8%, respectively, on the test dataset. CONCLUSIONS: The ability to correctly identify 12 out of 14 cases of OSA (with the 2 false negatives arising from subjects with an apnea-hypopnea index less than 10) indicates that the automated apnea classification system outlined may have clinical utility in pediatric patients.     

基于自适应学习的心律失常心拍分类方法

心律失常是因心脏疾病引起的心电活动中的异常症状,早期心室收缩（PVC）是由异位心跳引起的常见心律失常形式。通过心电图（ECG）信号检测PVC对于预测可能的心力衰竭具有重要意义。本文提出一种面向PVC心拍分类的心电信号分类算法,重点研究基于自适应学习的PVC异常心拍分类特征提取模型,通过计算心拍关联后验概率,结合领域专家标注信息训练分类器,提高整体分类效果。实验采用MIT-BIH心律失常数据库的ECG数据,研究结果表明所提方法针对非线性流形结构数据,能够有效提升小样本心拍自适应分类器的准确性。     

Modeling Respiratory Movement Signals During Central and Obstructive Sleep Apnea Events Using Electrocardiogram

Obstructive sleep apnea (OSA) causes a pause in airflow with continuing breathing effort. In contrast, central sleep apnea (CSA) event is not accompanied with breathing effort. CSA is recognized when respiratory effort falls below 15% of pre-event peak-to-peak amplitude of the respiratory effort. The aim of this study is to investigate whether a combination of respiratory sinus arrhythmia (RSA), ECG-derived respiration (EDR) from R-wave amplitudes and wavelet-based features of ECG signals during OSA and CSA can act as surrogate of changes in thoracic movement signal measured by respiratory inductance plethysmography (RIP). Therefore, RIP and ECG signals during 250 pre-scored OSA and 150 pre-scored CSA events, and 10 s preceding the events were collected from 17 patients. RSA, EDR, and wavelet decomposition of ECG signals at level 9 (0.15–0.32 Hz) were used as input to the support vector regression (SVR) model to recognize the RIP signals and classify OSA from CSA. Using cross-validation test, an optimal SVR (radial basis function kernel; C = 2⁸ and ε = 2⁻² where C is the coefficient for trade-off between empirical and structural risk and ε is the width of ε-insensitive region) showed that it correctly recognized 243/250 OSA and 139/150 CSA events (95.5% detection accuracy). Independent test was performed on 80 OSA and 80 CSA events from 12 patients. The independent test accuracies of OSA and CSA detections were found to be 92.5 and 95.0%, respectively. Results suggest superior performance of SVR using ECG as the surrogate in recognizing the reduction of respiratory movement during OSA and CSA. Results also indicate that ECG-based SVR model could act as a potential surrogate signal of respiratory movement during sleep-disordered breathing.     

Non-Episode-Dependent Assessment of Paroxysmal Atrial Fibrillation Through Measurement of RR Interval Dynamics and Atrial Premature Contractions

A method is presented for classifying a single lead surface electrocardiogram recording from a Holter monitor as being from a subject with paroxysmal atrial fibrillation (PAF) or not. The technique is based on first assessing the likelihood of 30-min segments of electrocardiogram (ECG) being from a subject with PAF, and then combining these per-segment likelihoods to form a per-subject classification. The per-segment assessment is based on the output of a supervised linear discriminant classifier (LDC) which has been trained using known data from the Physionet Atrial Fibrillation Prediction Database (which consists of two hundred 30-min segments of Holter ECG, taken from 53 subjects with PAF, and 47 without). One of two LDCs is used depending on whether there is a significant correlation between observed low-frequency and high-frequency spectral power in the RR power spectral density over the 30-min segment. If there is high correlation, then the LDC uses spectral features calculated over a 10-min window; in the low-correlation case, both spectral features and atrial premature contractions are used as features. The classifier was tested for its ability to distinguish PAF and non-PAF segments using three independent data sets (representing a total of 1370 segments from 50 subjects). The cumulative sensitivity, specificity, and accuracy on a per-segment basis were 43.0, 99.3, and 80.5%, respectively on these independent test sets. By combining the results of segment classification, a per-subject classification into PAF and non-PAF classes was performed. For the 50 subjects in the independent data sets, the sensitivity and specificity of the per-subject classifier were 100%.     

基于心电信号的自注意力双向门控循环网络疲劳检测模型

人类操作员的生理疲劳状态对其作业效率与安全性存在很大的影响,本研究提出了一种基于自注意力（SA）机制的双向门控循环（BiGRU）网络疲劳检测模型,研究基于心电信号的疲劳检测方法。首先采集了模拟不同负荷水平的过程控制任务环境下操作人员的心电数据,以一维心电数据作为输入,经过去噪预处理后,使用改进的BiGRU神经网络进行特征提取,BiGRU在保留GRU优点的同时可以更加充分学习心电信号前后时序的特征联系,并通过SA机制筛选显著相关特征信息,最后将所获得的特征信息经过softmax分类器,得到疲劳分类结果。与传统的GRU模型和BiLSTM模型进行了比较,经过改进后的SA-BiGRU模型的疲劳分类性能整体提高2%~5%,总体准确率达83%。     

Estimating daytime sleepiness with previous night electroencephalography,electrooculography, and electromyography spectrograms in patients with suspected sleep apnea using a convolutional neural network

A common symptom of obstructive sleep apnea (OSA) is excessive daytime sleepiness (EDS). The gold standard test for EDS is the multiple sleep latency test (MSLT). However, due to its high cost, MSLT is not routinely conducted for OSA patients and EDS is instead evaluated using sleep questionnaires. This is problematic however, since sleep questionnaires are subjective and correlate poorly with the MSLT. Therefore, new objective tools are needed for reliable evaluation of EDS. The aim of this study was to test our hypothesis that EDS can be estimated with neural network analysis of previous night polysomnographic signals. We trained a convolutional neural network (CNN) classifier using electroencephalography, electrooculography, and chin electromyography signals from 2,014 patients with suspected OSA. The CNN was trained to classify the patients into four sleepiness categories based on their mean sleep latency (MSL); severe (MSL < 5min), moderate (5 ≤ MSL < 10), mild (10 ≤ MSL < 15), and normal (MSL ≥ 15). The CNN classified patients to the four sleepiness categories with an overall accuracy of 60.6% and Cohen’s kappa value of 0.464. In two-group classification scheme with sleepy (MSL < 10 min) and non-sleepy (MSL ≥ 10) patients, the CNN achieved an accuracy of 77.2%, with sensitivity of 76.5%, and specificity of 77.9%. Our results show that previous night’s polysomnographic signals can be used for objective estimation of EDS with at least moderate accuracy. Since the diagnosis of OSA is currently confirmed by polysomnography, the classifier could be used simultaneously to get an objective estimate of the daytime sleepiness with minimal extra workload.     

Feature selection from nocturnal oximetry using genetic algorithms to assist in obstructive sleep apnoea diagnosis

Nocturnal pulse oximetry (NPO) has demonstrated to be a powerful tool to help in obstructive sleep apnoea (OSA) detection. However, additional analysis is needed to use NPO alone as an alternative to nocturnal polysomnography (NPSG), which is the gold standard for a definitive diagnosis. In the present study, we exhaustively analysed a database of blood oxygen saturation (SpO(2)) recordings (80 OSA-negative and 160 OSA-positive) to obtain further knowledge on the usefulness of NPO. Population set was randomly divided into training and test sets. A feature extraction stage was carried out: 16 features (time and frequency statistics and spectral and nonlinear features) were computed. A genetic algorithm (GA) approach was applied in the feature selection stage. Our methodology achieved 87.5% accuracy (90.6% sensitivity and 81.3% specificity) in the test set using a logistic regression (LR) classifier with a reduced number of complementary features (3 time domain statistics, 1 frequency domain statistic, 1 conventional spectral feature and 1 nonlinear feature) automatically selected by means of GAs. Our results improved diagnostic performance achieved with conventional oximetric indexes commonly used by physicians. We concluded that GAs could be an effective and robust tool to search for essential oximetric features that could enhance NPO in the context of OSA diagnosis.     

Automatic identification of sleep and wakefulness using single‐channel EEG and respiratory polygraphy signals for the diagnosis of obstructive sleep apnea

Polysomnography (PSG) is necessary for the accurate estimation of total sleep time (TST) and the calculation of the apnea–hypopnea index (AHI). In type III home sleep apnea testing (HSAT), TST is overestimated because of the lack of electrophysiological sleep recordings. The aim of this study was to evaluate the accuracy and reliability of a novel automated sleep/wake scoring algorithm combining a single electroencephalogram (EEG) channel with actimetry and HSAT signals. The study included 160 patients investigated by PSG for suspected obstructive sleep apnea (OSA). Each PSG was recorded and scored manually using American Academy of Sleep Medicine (AASM) rules. The automatic sleep/wake‐scoring algorithm was based on a single‐channel EEG (FP2‐A1) and the variability analysis of HSAT signals (airflow, snoring, actimetry, light and respiratory inductive plethysmography). Optimal detection thresholds were derived for each signal using a training set. Automatic and manual scorings were then compared epoch by epoch considering two states (sleep and wake). Cohen's kappa coefficient between the manual scoring and the proposed automatic algorithm was substantial, 0.74 ± 0.18, in separating wakefulness and sleep. The sensitivity, specificity and the positive and negative predictive values for the detection of wakefulness were 76.51% ± 21.67%, 95.48% ± 5.27%, 81.84% ± 15.42% and 93.85% ± 6.23% respectively. Compared with HSAT signals alone, AHI increased by 22.12% and 27 patients changed categories of OSA severity with the automatic sleep/wake‐scoring algorithm. Automatic sleep/wake detection using a single‐channel EEG combined with HSAT signals was a reliable method for TST estimation and improved AHI calculation compared with HSAT.     

Screening of obstructive sleep apnea using Hilbert–Huang decomposition of oronasal airway pressure recordings

Polysomnographic signals are usually recorded from patients exhibiting symptoms related to sleep disorders such as obstructive sleep apnea (OSA). Analysis of polysomnographic data allows for the determination of the type and severity of sleep apnea or other sleep-related disorders by a specialist or technician. The usual procedure entails an overnight recording several hours long. This paper presents a methodology to help with the screening of OSA using a 5-min oronasal airway pressure signal emanating from a polysomnographic recording during the awake period, eschewing the need for an overnight recording. The clinical sample consisted of a total of 41 subjects, 20 non-OSA individuals and 21 individuals with OSA. A signal analysis technique based on the Hilbert–Huang transform was used to extract intrinsic oscillatory modes from the signals. The frequency distribution of both the first mode and second mode and their sum were shown to differ significantly between non-OSA subjects and OSA patients. An index measure based on the distribution frequencies of the oscillatory modes yielded a sensitivity of 81.0% (for 95% specificity) for the detection of OSA. Two other index measures based on the relation between the area and the maximum of the 1st and 2nd halves of the frequency histogram both yielded a sensitivity of 76.2% (for 95% specificity).Although further tests will be needed to test the reproducibility of these results, the proposed measures seem to provide a fast method to screen OSA patients, thus reducing the costs and the waiting time for diagnosis.