嵌入式睡眠监测系统的设计与实现

设计了嵌入式睡眠监测系统,在不干扰睡眠的前提下,对睡眠者的脉搏信号进行采样,通过无线网络传输至上位机,利用LabVIEW搭建上位机虚拟仪器平台,实现脉搏信号的实时显示与分析;同时,利用上位机摄像头采集睡眠者睡眠过程的面部图像,存储与脉搏信号同步的图像信息,可提高对脉搏信号分析的可靠性;测试实验对比分析了睡眠期间分别使用该系统与传统方式所测得的心率参数,结果表明嵌入式睡眠监测系统对睡眠过程进行监测的实时性好、可靠性高,对睡眠质量的评估提供重要的参考依据。     

基于MSP430的便携式睡眠监测仪的设计

睡眠质量对人的健康至关重要,为减小多导睡眠图仪监测睡眠时给患者带来的生理心理负荷,采用了一种仅通过监测双光源手指脉搏信号来完成患者睡眠质量监测的方法.通过硬件电路采集整夜的双光源手指脉搏波信号,再用上位机软件对信号进行分析,从中分离出睡眠相关信息,从而对睡眠质量进行客观评估.该方法对睡眠几乎没有影响,记录方法简单、操作...     

基于信号质量检测的腕式生理监测系统

设计了一种基于信号检测的腕式生理监测系统,嵌入一种基于循环自相关的信号检测算法,用于检测腕式生理监测系统的佩戴状态.依据信号质量参数,将信号标记为佩戴和不佩戴2种状态.佩戴状态时,计算并显示时间和脉率;不佩戴时,显示时间,脉率显示为0.实验表明:监测系统脉率显示更加精细、精准化.同时,将腕表与Phillip DB18进行了对比实验,脉率的准确度为±2 bpm.     

基于 GPRS和Zigbee的无线心电信号监测系统设计

针对疫情下医护人员对病人身体各种参数监测不便和工作效率低下等问题,提出了一种基于GPRS和Zigbee的无线心电信号监测系统;系统利用脉搏传感器作为检测终端可同时监测多个病人的生理状况数据,检测终端对数据进行汇总分析、存储和显示同时在紧急情况下进行呼救等功能,同时系统可将不同终端得到的生理参数通过ZigBee组网传输给GPRS组网的协调器,协调器可以将数据接入互联网上传至服务器和系统设计的上位机;通过对系统一个终端的软硬件设计测试,系统能准确测量不同病人的生理信息;通过数据对比精确度可达到2%;可以证明该系统有很好的医学应用前景。     

基于LabVIEW的光电容积脉搏波信号采集系统

光电容积脉搏波包含了人体丰富的生理、病理信息,对其进行实时监测可为临床研究和诊断提供科学的指导.开发了一套基于图形化虚拟仪器工程设计平台LabVIEW的光电容积脉搏波信号采集系统,可完成对该信号的实时采集、显示和数据存储.经过指端光电容积脉搏波信号的透射式采集实验,在LabVIEW前面板上准确显示出了该信号的波形,有助...     

基于WindowsCE的嵌入式多生理参数采集显示系统

实现了一种能对多种生理参数进行实时检测、存储、分析、显示及报警的嵌入式系统,采用S3C2440作为核心控制器,使用CAN总线实时传递生理参数信号,用WindowsCE作为操作系统协调软件界面与硬件的运作;图形用户软件界面考虑到生理参数数据量大,实时性要求高的特点,利用自定义的消息方式和多线程技术充分提高程序的实时性和代码的执行效率;程序运用面向对象的思想,便于后续功能的开发;通过和高实时性的PC测试程序波形比较表明,本系统具有很强的实时性和稳定性,可以实现预期的监测功能。     

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。     

床垫式睡眠监测系统及信号处理方法

为解决非接触式睡眠监测系统中混合信号的可靠获取以及生理特征参数的有效分离和识别等问题,采用压电薄膜传感器获取人体睡眠状态下压力信号,并采用电荷放大电路和信号调理电路进行实时采集;信号处理过程中先利用经验小波变换方法分离心冲击(BCG)和呼吸信号等单一模态分量,然后使用K-means算法对分离出的心冲击信号中不同类型的波峰聚类,进而通过平均心跳周期计算心率.实验结果表明,所设计的监测系统具有较强的自适应性,能有效提取呼吸和心跳信号.     

基于Android的麻醉深度检测仪设计与实现

麻醉深度监测在手术室和重症监护病房起着非常重要的作用。为了方便医护人员在手术或重症监护过程中对患者的意识状态进行准确的评估,设计了一种简易的基于Android的麻醉深度检测系统。该系统主要包含三个部分:对前端采集的EEG信号进行放大滤波;对采集到的信号进行排列熵算法处理;Android人机接口界面的设计。在完成整体方案设计后,对整个系统进行了测试,并利用麻省理工学院的生理信号数据库的多导睡眠EEG数据进行测试,实验结果说明该系统能够反映病人的脑电意识状态。     

基于WBAN的智能康复监测护理系统设计和实现

针对传统生理参数监测的不足,基于无线体域网（WBAN）设计并实现了用于康复训练的智能康复监测护理系统。本文首先根据人体生理信号检测原理检测人体的心电、皮肤电阻、脉搏和体温信号,然后通过Crossbow无线传感器网络平台将检测到的数据实时传输到上位机,最后在上位机监控软件上实现对患者的生理信号实时显示和监控。文中详细介绍了系统的软、硬件设计,并对实验结果进行了处理和分析,实验结果表明该系统能对患者的生理状态进行实时监测。     

Detecting rapid eye movement sleep using a single EEG signal channel

Sleep stage scoring is generally determined in a polysomnographic (PSG) study where technologists use electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals to determine the sleep stages. Such a process is time consuming and labor intensive. To reduce the workload and to improve the sleep stage scoring performance of sleep experts, this paper introduces an intelligent rapid eye movement (REM) sleep detection method that requires only a single EEG channel. The proposed approach distinguishes itself from previous automatic sleep staging methods by introducing two sets of auxiliary features to help resolve the difficulties caused by interpersonal EEG signal differences. In addition to adopting conventional time and frequency domain features, two empirical rules are introduced to enhance REM detection performance based on sleep being a continuous process. The approach was tested with 779,661 epochs obtained from 947 overnight PSG studies. The REM sleep detection results show a kappa coefficient at 0.752, an accuracy level of 0.930, a sensitivity score of 0.814, and a positive predictive value of 0.775. The results also show that the performance of the approach varies with the ratio of REM sleep and the severity of sleep apnea of the subjects. The experimental results also show that it is possible to improve the performance of an automatic sleep staging method by tailoring it to subgroups of persons that have similar sleep architecture and clinical characteristics.     

小波变换在电力谐波分析中的应用

电力系统的谐波是影响电能质量的重要因素,谐波对电力系统和用电设备产生了严重危害和影响。文中应用小波变换分析电力系统的谐波,小波变换能描述频谱含量如何随着时间变化,同时在时间和频率上表示信号的能量和作用。与傅里叶变换对比,小波变换不仅可以知道哪些频率分量在信号中出现,而且可以知道这些频率分量在时域内是如何变化的,可以更精确地分析非平稳信号的谐波。     

基于周期分割的睡眠自动分期研究

为实现高效的自动睡眠分期，提出一种基于周期分割的时域信号处理方法，采用合并增减序列方法对三个通道多导睡眠图记录（2路脑电，1路眼电）进行周期分割，根据信号波形的周期标记睡眠各期的特征波形，提取特征波形在每一帧数据的时长占比与平均幅值作为特征。双向长短时记忆网络（Bi-directional Long Short-Term Memory，Bi-LSTM）作为分类器，解决传统机器学习方法无法利用睡眠数据时间上下文信息的缺点。对42?699个样本使用交叉验证方法得到了84.8%的平均准确率，实验结果表明合并增减序列方法可以降低脑电信号分析的复杂度，是一种有效的时域信号处理方法，双向长短时记忆网络可以有效提高睡眠分期准确率，具有良好的应用前景。     

非线性格兰杰因果关系在睡眠生理信号分析中的应用

基于非线性格兰杰因果关系分析睡眠生理信号。分别使用多项式核函数、高斯核函数和Sigmoid核函数将低维空间数据映射到高维特征空间,在高维特征空间使用非线性格兰杰因果方法来分析睡眠生理信号。研究结果表明,脑电信号对心电信号的影响比心电信号对脑电信号的影响更为显著,脑电信号对血压信号的影响比血压信号对脑电信号的影响更为显著,血压对心电信号的影响比心电信号对血压信号的影响更为显著,而且睡眠期样本信号间的格兰杰因果关系更为显著。仿真结果验证了睡眠期信号更能客观地反映生理信号的因果关系。     

Automatic sleep staging using empirical mode decomposition,discrete wavelet transform,time-domain,and nonlinear dynamics features of heart rate variability signals

The conventional method for sleep staging is to analyze polysomnograms (PSGs) recorded in a sleep lab. The electroencephalogram (EEG) is one of the most important signals in PSGs but recording and analysis of this signal presents a number of technical challenges, especially at home. Instead, electrocardiograms (ECGs) are much easier to record and may offer an attractive alternative for home sleep monitoring. The heart rate variability (HRV) signal proves suitable for automatic sleep staging. Thirty PSGs from the Sleep Heart Health Study (SHHS) database were used. Three feature sets were extracted from 5- and 0.5-min HRV segments: time-domain features, nonlinear-dynamics features and time–frequency features. The latter was achieved by using empirical mode decomposition (EMD) and discrete wavelet transform (DWT) methods. Normalized energies in important frequency bands of HRV signals were computed using time–frequency methods. ANOVA and t-test were used for statistical evaluations. Automatic sleep staging was based on HRV signal features. The ANOVA followed by a post hoc Bonferroni was used for individual feature assessment. Most features were beneficial for sleep staging. A t-test was used to compare the means of extracted features in 5- and 0.5-min HRV segments. The results showed that the extracted features means were statistically similar for a small number of features. A separability measure showed that time–frequency features, especially EMD features, had larger separation than others. There was not a sizable difference in separability of linear features between 5- and 0.5-min HRV segments but separability of nonlinear features, especially EMD features, decreased in 0.5-min HRV segments. HRV signal features were classified by linear discriminant (LD) and quadratic discriminant (QD) methods. Classification results based on features from 5-min segments surpassed those obtained from 0.5-min segments. The best result was obtained from features using 5-min HRV segments classified by the LD classifier. A combination of linear/nonlinear features from HRV signals is effective in automatic sleep staging. Moreover, time–frequency features are more informative than others. In addition, a separability measure and classification results showed that HRV signal features, especially nonlinear features, extracted from 5-min segments are more discriminative than those from 0.5-min segments in automatic sleep staging.     

基于经验小波变换的BCG信号提取方法研究

为解决非接触式睡眠监测系统中呼吸和心跳信号的有效分离和准确提取问题,采用经验小波变换手段,根据信号频谱特征利用尺度空间变换实现频域的自适应划分,然后依据频谱划分的边界构造正交小波滤波器组,实现了从所获取的混合压力信号中有效提取出心冲击和呼吸信号等单模态分量。初步实验结果表明,与常规滤波方法相比,该方法具有较高的自适应性...     

Detection of high-frequency respiratory movements during sleep

Sleep-related breathing disorders are common in adults and they have a significant impact on vigilance and quality of life. Previous studies have shown the validity of the static-charge-sensitive bed (SCSB) in monitoring breathing abnormalities during sleep. A whole nights sleep study produces a signal with considerable length, and therefore an automated analysis system would be of great need. In this work we focus on detection of high-frequency respiratory movement (HFRM) patterns which are related to increased respiratory efforts. The paper documents four methods to automatically detect these patterns. The first two are based on classical statistical tests applied to the SCSB signal, and the other two use spectral characteristics in order to adaptively segment the SCSB signal. Finally we adjust each method to detect patterns that coincide with the HFRMs determined by an expert, and evaluate the performance of the methods using independent test data.     

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.     

Classification of sleep stages based on LSTAR model

Sleep study is very important in the health since sleep disorders affect the productivity of individuals. One of the important topics in sleep research is the classification of sleep stages using the electroencephalogram (EEG) signal. Electrical activities of brain are measured by EEG signal in the laboratory. In real-world environments, EEG signal is also used in portable monitoring devices to analyze sleep. In this study, we propose an efficient method for classification of sleep stages. EEG signals are examined by a new model from autoregressive (AR) family, namely logistic smooth transition autoregressive (LSTAR) to study sleep process. In contrast to the AR model, LSTAR is a non-linear one; therefore, it is suitable for modeling non-linear signals such as EEG. In the current research, at first, each 30-second epoch of EEG signal is decomposed into the time-frequency sub-bands using the double-density dual-tree discrete wavelet transform (D3TDWT). In the second step, LSTAR model is used for feature extraction from each sub-band. Next, the dimension of feature vector is reduced by tensor locality preserving projection (tensor LPP) method, and then the obtained features are given to classifier to determine the stage of each epoch based on the number of considered classes. After classifying sleep stages, some misclassified epochs can be corrected according to the smoothing rule. We consider different classifiers and evaluate their performance. The results indicate the efficiency of the proposed method in comparison with the recently introduced methods in terms of accuracy and Kappa coefficient.