基于光电容积脉搏波的呼吸波提

本研究通过人体指端的光电容积脉搏波,提取呼吸波信号。同时采用小波分析和经验模态分解方法对脉搏波信号进行分解并重构呼吸信号,然后与采集的呼吸波信号做相关性分析。通过对5名志愿者的实验,结果显示采用经验模式分解方法所提取的呼吸波具有更好的相关性。通过分析数据显示,其波形相关系数在0.5左右,AR频谱相关系数在0.8以上。由此可以证明,经验模式分解法可有效提取人体指端光电容积脉搏波中所包含的呼吸波成分。     

基于光电容积脉搏波的呼吸监测系统研究

通过研究人体指端光电容积脉搏波(PPG)的物理特性,以朗伯比尔定律为基本原理,通过设计硬件电路,实现对人体PPG的无创采集。对采集到的PPG信号进行滤波和放大处理,通过特征选择和提取等方式识别出比较完整的脉搏波信号;将得到的脉搏波信号进行经验模态分解,选择具有适当频率的本征模函数重构出待测的呼吸波信号,并在显示屏上显示出来。在采集脉搏波的同时利用迈瑞公司的PM-9000 Express病人监护仪对人体的呼吸信号进行采集。本系统采集了10例志愿者数据。将本系统得到的呼吸波信号与病人监护仪测得呼吸波进行频谱分析,并对相关参数进行比较,发现两种呼吸波具有较好的相关性。本文最终结果表明利用经验模态分解方法提取人体PPG中包含的呼吸信号具有较好的准确性和可行性。实验结果表明这种方法可以从PPG中提取呼吸信号。     

基于光电容积脉搏波的呼吸频率监测

目的呼吸频率是人体生理信息监测中一个重要的生理指标,做好呼吸频率的监测对于一些疾病的及时诊疗有重大意义。光电容积脉搏波中包含许多生理信息,而本研究即从光电容积脉搏波中提取呼吸频率。方法首先识别出光电容积脉搏波,提取出脉搏波的包络、间隔、幅度、面积信号4个特征参数,并利用三次样条插值和频谱分析从中提取各自对应的频率,采用加权平均得到最终的呼吸频率,并将本文提取的呼吸频率同给定的标准呼吸频率进行对比。结果在时域和频域具有较好的相关性,并且不同数据多次测量的统计结果显示测量误差较小。结论本方法能有效地从光电容积脉搏中提取呼吸频率,且计算简单,为实现呼吸频率的实时、连续、无创监测提供了一定的理论依据。     

一种利用经验模态分解算法的光电容积脉搏波信号中提取呼吸波的方法研究

针对目前提取呼吸波准确性不高的问题,本研究提出了一种从光电容积描记(photoplethysmography, PPG)信号中提取呼吸波的有效方法。在MIMIC Database中获取人体同时段的多路生理信号,包括PPG信号和呼吸波信号。首先,利用经验模态分解算法(empirical mode decomposition,EMD)对PPG信号进行分解,得到各层本征模函数(intrinsic mode function,IMF),选择合适的IMF分量重构出呼吸波信号;然后将重构的呼吸波信号与采用PPG信号同时段的原始呼吸波信号进行比较,结果显示,呼吸波信号速率的准确率均在90%以上,AR功率谱中的相关性系数均在85%以上,呼吸波信号相对相干系数也显示该方法的优越性。采用EMD算法可以有效地从PPG信号中提取呼吸波,这对于临床实践中的无创检测,医疗设备的改进具有重要意义。     

一种同时消除脉搏波信号中呼吸基线漂移和高频噪声的方法

目的：基于光电容积脉搏波可以实现血氧饱和度等人体生理参数的无创检测。基于光电容积脉搏波测量时,由于信号采集过程中存在人体呼吸和仪器本身热噪声等干扰,脉搏波信号中存在着呼吸基线漂移和高频噪声,影响最终的人体生理参数测量精度。方法：因此提出一种在经验模式分解的过程中结合小波变换的方法,来同时消除呼吸基线漂移和高频噪声的影响。首先通过经验模态分解将脉搏波信号分解为若干内在模式分量,并分别判断出含有呼吸基线漂移和代表高频噪声的分量,对于代表高频噪声的分量采用类似小波变换的方法进行滤波,利用小波变换将含有呼吸基线漂移的分量分解,将代表呼吸基线漂移的小波细节置零,信号重构后就达到了同时消除呼吸基线和高频噪声的目的。利用自行研制的测量装置采集的脉搏波信号进行实验验证,并采用信号交直流比R和信号的频谱进行效果评价。结果：有效地同时消除了呼吸基线漂移和高频噪声。结论：该方法将有利于血氧饱和度等人体生理参数无创检测精度的提高。     

基于超声回波信号的指端脉搏波提取

脉搏波可作为检测人体心血管系统生理病理状态的重要依据。为了验证用超声波测量脉搏波的可能、解决脉搏波的测量部位受限的问题,本研究提出一种从超声回波信号中提取脉搏波的方法。设计一种跟随式超声传感器,用数据采集系统采集指端超声回波信号,经过滤波、选点及小波去噪等处理后得到较为纯净的脉搏波信号;同时采集心电信号以及光电容积脉搏波信号作为参考信号。结果表明,可以从提取的指端脉搏波中准确地获取心率;与同步测得的光电容积脉搏波数据相关系数大部分在0.8以上;波形中的重搏前波、重搏波等细节部分也能明显地表现出来。本研究提出的方法实现了从指端超声回波信号中获取完整可靠的脉搏波信号,为日后获取不同部位的脉搏信号提供了基础。     

基于光电容积脉搏波特征信息的睡眠呼吸事件判别

研究脉搏波特征信息与睡眠呼吸事件的关系,实现对呼吸事件的初步判别。采用小波变换的方法分析光电容积脉搏波,提取特征参量,结合专家经验校正后的呼吸事件分布,寻找这些参量与睡眠呼吸事件的相关关系,确立呼吸事件的初步判据,并用于呼吸事件初步判别。研究了5位受试者夜间脉搏波信号,专家校正的一晚呼吸事件总数为1239次。结果表明,呼吸事件初步判别的符合率在91%以上,尤其对于患有中重度OSAHS的病人,符合率可达96.1%。本研究提供了利用脉搏波特征参量进行呼吸事件初步判别的理论根据和实现方法。     

HHT方法在脉搏波信号分析中的应用

目的采用HHT(Hibert-HuangTransformation)时间序列分析方法处理从人体采集到的脉搏波信号。方法通过经验模态分解(EMD)技术将一非线性、非稳态过程的原始离散数据序列分解为一组内在模态函数(IMFs),然后对每一个IMF进行HT变换,这样得到的信号幅度和瞬时频率都是时间的函数,即获得脉搏波信号幅度和频率的时间分布。再根据已获得的HH谱,进而得到边际谱。这是一种更具适应性的、新型的、基于模态分解的时间序列数据处理方法。结果首先对一系列由标准的周期函数构造而成的时间序列信号进行了EMD处理,验证HHT方法分解的可行性、有效性;然后分别对一例正常人脉搏波信号和一例典型的冠心病人脉搏波信号进行分解处理,对得到结果进行了比较。结论HHT方法在生物医学信号处理领域将会有广阔的应用前景。     

非接触式心率测量研究初步

基于Matlab软件平台,通过图像光电容积脉搏波描记法(iPPG),提取心率波信号。结合人脸检测与跟踪技术,选取脸部三角区域作为感兴趣区域 (ROI),从中分析提取iPPG信号,然后采用快速傅里叶变换计算出心率。通过对20名志愿者的实验,结果显示在不同颜色通道(R、G、B)提取出的心率信号的信噪比不同,其中G通道的心率信号最强。分析数据显示,与医用心电测量仪相比,该方法测量的平均误差为1.73 beat/min。采用光容积成像技术检测方法,可进一步实现血液灌注三维可视化,能检测动脉血氧饱和度、呼吸率、神经系统的生理活动和新生儿监护,甚至能应用于家庭移动医疗、运动检测等大众场合,提高便捷性,降低医疗成本。     

两种计算正常成年人短时心率变异性方法的比较研究

目的利用光电容积脉搏波法分析心率变异性,为通过心率变异性(heart rate variability,HRV)参数表征自主神经系统的变化提供新的研究方法。方法对46个健康成年受试者分别同步采集自主呼吸状态下的心电信号和光电容积脉搏波信号,利用二阶差分极大值提取心电图的RR间期,搜索脉搏波极大值提取脉搏波主波波峰间期PP,然后计算通用HRV参数SDNN、RMSSD和LF/HF,并利用回归的统计学方法结合Bland-Altman随机分析法对这两种方法获取的HRV结果进行比较。结果两种方法计算得到的心率变异性参数结果的Pearson相关系数分别为0.998、0.995和0.992,均显著相关(P0.01),Bland-Altman分布图也均在一致性区间内,说明这两种方法在计算心率变异性上具有一致性。结论利用光电容积脉搏波法分析心率变异具有可行性。     

基于HHT的生物电阻抗胃动力信号分析方法

目的 采用HHT时间序列分析方法处理从人体采集到的胃动力信号.方法 通过经验模态分解(EMD)技术将一非线性、非稳态过程的原始胃动力序列分解为一组内在模态函数(IMFs),对每一个IMF进行Hilbert 变换,得到信号的瞬时频率,然后选择与胃动力相关的频率成分,即0.03-0.06 Hz之间的IMF进行重构提取胃动力...     

基于聚偏氟乙烯薄膜的穿戴式呼吸检测系统

【摘 要】 针对人体呼吸信号的特点设计一种基于聚偏氟乙烯（PVDF）压电薄膜材料的可穿戴式呼吸检测系统。人体呼吸时PVDF薄膜受力产生的感应电荷较少,经信号调理电路将电荷量转换成电压量。单片机通过模数转换器获得呼吸信号数据,通过蓝牙发送给上位机。上位机从获得的数据中提取呼吸波形,并进行平滑滤波、自适应双阈值来计算呼吸率。试验结果表明,本系统可以实时准确地检测出人体的呼吸波形,呼吸次数识别的准确率在90%以上,可以满足人体呼吸监护的需求。     

Tissue Artifact Removal from Respiratory Signals Based on Empirical Mode Decomposition

On-line measurement of respiration plays an important role in monitoring human physical activities. Such measurement commonly employs sensing belts secured around the rib cage and abdomen of the test object. Affected by the movement of body tissues, respiratory signals typically have a low signal-to-noise ratio. Removing tissue artifacts therefore is critical to ensuring effective respiration analysis. This paper presents a signal decomposition technique for tissue artifact removal from respiratory signals, based on the empirical mode decomposition (EMD). An algorithm based on the mutual information and power criteria was devised to automatically select appropriate intrinsic mode functions for tissue artifact removal and respiratory signal reconstruction. Performance of the EMD-algorithm was evaluated through simulations and real-life experiments (N = 105). Comparison with low-pass filtering that has been conventionally applied confirmed the effectiveness of the technique in tissue artifacts removal.     

Neural network for photoplethysmographic respiratory rate monitoring

The reflection mode photoplethysmographic (PPG) signal was studied with the aim of determining respiratory rate. The PPG signal includes respiratory synchronous components, seen as frequency modulation of the heart rate (respiratory sinus arrhythmia), amplitude modulation of the cardiac pulse and respiratory-induced intensity variations (RIIVs) in the PPG baseline. PPG signals were recorded from the foreheads of 15 healthy subjects. From these signals, the systolic wavefrm diastolic waveform, respiratory sinus arrhythmia, pulse amplitude and RIIVs were extracted. Using basic algorithms, the rates of false positive and false negative detection of breaths were calculated separately for each of the five components. Furthermore, a neural network was assessed in a combined pattern recognition approach. The error rates (sum of false positive and false negative breath detections) for the basic algorithms ranged from 9.7% (pulse amplitude) to 14.5% (systolic waveform). The corresponding values for the neural network analysis were 9.5–9.6%. These results suggest the use of a combined PPG system for simultaneous monitoring of respiratory rate and arterial oxygen saturation (pulse oximetry).     

Respiratory variations in the reflection mode photoplethysmographic signal. Relationships to peripheral venous pressure

Photoplethysmography (PPG) is a non-invasive optical way of measuring variations in blood volume and perfusion in the tissue, used in pulse oximetry for instance. Respiratory-induced intensity variations (RIIVs) in the PPG signal exist, but the physiological background is not fully understood. Respiration causes variations in the blood volume in the peripheral vascular bed. It was hypothesised that the filling of peripheral veins is one of the important factors involved. In 16 healthy subjects, the respiratory synchronous variations from a PPG reflection mode signal and the peripheral venous pressure (PVP) were recorded. Variations of tidal volume, respiratory rate and contribution from abdominal and thoracic muscles gave significant and similar amplitude changes in both RIIV and the respiratory variation of PVP (p<0.01). The highest amplitudes of both signals were found at the largest tidal volume, lowest respiratory rate and during mainly thoracic breathing, respectively. The coherence between PVP and RIIV signals was high, the median (quartile range) being 0.78 (0.42). Phase analysis showed that RIIV was usually leading PVP, but variations between subjects were large. Although respiratory-induced variations in PVP and PPG showed a close correlation in amplitude variation, a causal relationship between the signals could not be demonstrated.     

Estimation of respiratory volumes from the photoplethysmographic signal. Part I: experimental results

To evaluate the possibility of respiratory-volume measurement using photoplethysmography (PPG), PPG signals from 16 normal volunteers are collected, and the respiratory-induced intensity variations (RIIV) are digitally extracted. The RIIV signals are studied while reepiratory volume is varied. Furthermore, respiratory rate, body posture and type of respiration are varied. A Fleisch pneumotachograph is used as the inspired volume reference. The RIIV and pneumotachography signals are compared, and a statisical analysis is performed (linear regression and t-tests). The key idea is that the amplitude of the RIIV signal is related to the respiratory volume. The conclusion from the measurements is that there exists a relationship between the amplitude of the RIIV signal and the respiratory volume (R=0.842, s=0.428, p<0.005). Absolute measurements of the respiratory volume are not possible from the RIIV signal with the present set-up. The RIIV signal also seems to be affected by respiratory rate and type. More knowledge about respiratory parameters and improved sensor and filter design are required to make absolute measurements of volumes possible.     

基于经验模态分解-小波包变换的表面肌电信号手势识别

为了提高表面肌电信号（sEMG）手部运动识别的正确率,比较常规的sEMG预处理和特征提取方法,提出一种基于经验模态分解（EMD）和小波包变换（WPT）的sEMG手势识别模型。首先,使用EMD方法将sEMG进行平稳化,得到一系列的固有模态函数。其次,求取各个固有模态函数与原始信号的相关性,选取相关性较高的前4个分量作为有效分量。然后,采用Db3小波函数进行WPT,提取小波包系数中的平均能量、平均绝对值、最大值、均方根和方差等特征。分别采用线性判别分析和支持向量机对12种手部运动进行模式识别。结果表明基于EMD和WPT的sEMG手势识别正确率比直接提取小波包系数中的特征识别正确率高。     

Investigating Relative Respiratory Effort Signals During Mixed Sleep Apnea Using Photoplethysmogram

Sleep disordered breathing does show different types of events. These are obstructive apnea events, central apnea events and mixed sleep apnea (MSA) which have a central component with a pause in airflow without respiratory effort followed by an obstructive component with respiratory effort. The esophageal pressure (Pes) is the accurate method to assess respiratory effort. The aim of the present study is to investigate whether the features extracted from photo-plethysmogram (PPG) could relate with the changes in Pes during MSA. Therefore, Pes and PPG signals during 65 pre-scored MSA events and 10 s preceding the events were collected from 8 patients. Pulse intervals (PPI), Pulse wave amplitudes (PWA) and wavelet decomposition (Wv) of PPG signals at level 8 (0.15–0.32 Hz) were derived from PPG signals. Results show that significant correlations (r = 0.63, p < 0.01; r = 0.42, p < 0.05; r = 0.8, p < 0.01 for OSA part) were found between reductions in Pes and that in PPG based surrogate respiratory signals PPI, PWA and Wv. Results suggest that PPG based relative respiratory effort signal can be considered as an alternative to Pes as a means of measuring changes in inspiratory effort when scoring OSA and CSA parts of MSA events.     

Preservation of quadrature Doppler signals from bidirectional slow blood flow close to the vessel wall using an adaptive decomposition algorithm

A novel approach based on the phasing-filter (PF) technique and the empirical mode decomposition (EMD) algorithm is proposed to preserve quadrature Doppler signal components from bidirectional slow blood flow close to the vessel wall. Bidirectional mixed Doppler ultrasound signals, which were echoed from the forward and reverse moving blood and vessel wall, were initially separated to avoid the phase distortion of quadrature Doppler signals (which is induced from direct decomposition by the nonlinear EMD processing). Separated unidirectional mixed Doppler signals were decomposed into intrinsic mode functions (IMFs) using the EMD algorithm and the relevant IMFs that contribute to blood flow components were identified and summed to give the blood flow signals, whereby only the components from the bidirectional slow blood flow close to the vessel wall were retained independently. The complex quadrature Doppler blood flow signal was reconstructed from a combination of the extracted unidirectional Doppler blood flow signals. The proposed approach was applied to simulated and clinical Doppler signals. It is concluded from the experimental results that this approach is practical for the preservation of quadrature Doppler signal components from the bidirectional slow blood flow close to the vessel wall, and may provide more diagnostic information for the diagnosis and treatment of vascular diseases.     

Deriving respiration from photoplethysmographic pulse width

A method for deriving respiration from the pulse photoplethysmographic (PPG) signal is presented. This method is based on the pulse width variability (PWV), and it exploits the respiratory information present in the pulse wave velocity and dispersion. It allows to estimate respiration signal from only a pulse oximeter which is a cheap and comfortable sensor. Evaluation is performed over a database containing electrocardiogram (ECG), blood pressure (BP), PPG, and respiratory signals simultaneously recorded in 17 subjects during a tilt table test. Respiratory rate estimation error is computed obtaining of 1.27 ± 7.81 % (0.14 ± 14.78 mHz). For comparison purposes, we have also obtained a respiratory rate estimation from other known methods which involve ECG, BP, or also PPG signals. In addition, we have also combined respiratory information derived from different methods which involve only PPG signal, obtaining a respiratory rate error of ?0.17 ± 6.67 % (?2.16 ± 12.69 mHz). The presented methods, PWV and combination of PPG derived respiration methods, avoid the need of ECG to derive respiration without degradation of the obtained estimates, so it is possible to have reliable respiration rate estimates from just the PPG signal.