脉搏血氧饱和度检测中自适应滤波消除运动伪差的方法研究

目的研究脉搏血氧饱和度检测系统中运动伪差的消除方法,以提高脉搏血氧仪检测性能。方法通过脉搏血氧仪中的双光束构造噪声参考信号,利用最小均方自适应滤波法消除运动伪差干扰的影响。结果建立了脉搏血氧饱和度检测中消除运动伪差的计算方法,可成功地从运动伪差中提取正常光电容积脉搏波信号作为计算氧饱和度的依据。结论该计算方法简单,可用于实时处理,且测量结果可靠,为进一步抑制脉搏血氧仪噪声奠定了基础。     

基于DTCWT和cICA的光电容积脉搏波运动干扰消除算法

目的:随着穿戴医疗设备普遍被接受,在利用光电容积脉搏波描记法(PPG)测量血氧、心率等生理参数时,运动干扰与脉搏信号频率混叠问题尤为突出,为了在日常活动状态下得到准确的生理参数,消除运动干扰是最为重要的手段。方法:提出了一种基于双树复小波变换(DTCWT)和约束独立成分分析(cICA)的组合算法消除运动干扰。首先用DTCWT将含有运动干扰的两路(红光和红外)PPG信号分解为若干不同频带的分量;然后通过cICA方法,提取感兴趣的脉搏成分;最后通过最小均方误差自适应滤波器实现两路PPG信号重建。结果:由DTCWT+cICA恢复的PPG波形得到的心率值与无运动干扰时基本一致,而血氧饱和度值也与无运动干扰时最接近。结论:与DTCWT和cICA相比,DTCWT和cICA组合算法能更有效地消除PPG中的运动干扰。实验结果验证了方法的可行性和有效性。     

脉搏血氧饱和度检测系统设计中干扰信号的处理方法

脉搏血氧饱和度检测系统是一种将血氧浓度的光电检测技术与容积脉搏描记技术结合起来实现无创伤、连续血氧浓度测量的新型医疗监护仪器。消除测量系统中各种干扰的影响 ,设法正确地检出光电容积脉搏波信号是关系到测量系统准确性及其精度的关键问题之一。本文着重介绍了脉搏血氧饱和度检测系统中干扰的主要来源、处理方法及其应用现状 ,从而为脉搏血氧饱和度检测系统设计提供了依据。     

脉搏血氧饱和度检测系统设计中干扰信号的处理方法

脉搏血氧饱和度检测系统是一种将血氧浓度的光电检测技术与容积脉搏描记技术结合起来实现无创伤、连续血氧浓度测量的新型医疗监护仪器。消除测量系统中各种干扰的影响,设法正确地检出光电容积脉搏波信号是关键到测量准确性及其精度的关键问题之一。本着重介绍了脉搏血氧饱和度检测系统中干扰的主要来源,处理方法及其应用现状,从而为脉搏血氧饱和度检测系统设计提供了依据。     

基于小波阈值法的脉搏波去噪算法

目的消除可穿戴式脉搏波监测设备在连续测量中由于运动造成的运动伪差,保证设备准确性和稳定性。方法通过选取合适的小波基、小波最大分解层数、阈值函数和阈值方法,对脉搏波信号进行小波阈值处理,提出了一种基于小波阈值法去除脉搏波噪声的算法。并针对在脉搏波信号采集过程中出现的基线漂移、工频干扰和运动伪差,与加窗傅里叶变换去噪后的结果进行对比。结果在信噪比、均方差和平滑度等关键指标上,小波阈值法的效果更优。利用db9小波基对脉搏波信号进行6层小波分解,设置启发式阈值所得到的处理效果最好。结论该算法能够有效抑制工频干扰和运动干扰,使信噪比提高22 dB,均方差接近于0,且平滑度降为原来的11%,实现脉搏波信号采集中干扰的有效去除。     

可穿戴式多参数监护装置信号处理平台的设计与实现

背景：可穿戴式多参数监护装置具有生理信号检测和处理、信号特征提取和数据传输等基本功能模块,可实现对人体的无创检测、诊断。 目的：将信号处理平台运用到对时效性和精确度要求较高的可穿戴式多参数监护装置中,提高ECG信号QRS波检测的检测速度和检定准确率。 方法：提出了一种新型可穿戴式多参数监护装置信号处理平台的设计思路,应用TMS320VC5509系列DSP系统实现改进后的LADT压缩算法结合小波变换和阈值检测ECG信号中QRS波的方法。 结果与结论：采用硬件DSP的方法显著提高了QRS波检测的速度,其结果可以用于穿戴式多参数监护装置异常心电检测的实际应用。     

反射式血氧模拟仪设计

目的研发基于光电传感器的反射式血氧模拟仪,以满足反射式血氧设备的评估和测试要求。方法首先根据血氧设备的测量原理,研制反射式血氧模拟仪。该设备通过采集反射式血氧设备的红光、红外信号光强和频率,产生不同强度的波形以模拟血氧值,实现测试反射式血氧设备的功能。然后选择两款反射式血氧测量设备用于反射式模拟仪测试,以检测反射式血氧模拟仪的输出范围和可靠性。结果两款反射式血氧测量设备可识别模拟仪输出的70～100之间的血氧信号,并能显示出血氧值;两款设备显示的波形与反射式模拟仪输出的脉搏波基本一致。结论在工程实践中,反射式血氧模拟仪能够模拟人体的血氧饱和度,在测试反射式血氧设备方面是可靠的,可满足工程实践要求。     

基于GPRS的腕带式儿童健康实时监护模块的研究

目的:研制一种基于GPRS的应用于儿童监护的腕带式装置,此装置通过对儿童生理信号的实时检测,可以让父母实时了解孩子的身体状况.方法:此监护装置通过石英晶体压力传感器采集脉搏波信号和DS18820采集温度信号,并通过GPRS模块把信号无线发射至中央服务器或父母的手机端.结果:该监护模块有效完成了信号的采集和无线发射.并具有稳定性好、可穿戴式、低功耗和狂干扰性强等优点.结论:基于GPRS的无线穿戴监护系统的使用不受地域限制,使得儿童在发生意外或疾病时,能及时得到救助.随着社会对儿童健康和安全关注度的提高,这种设备的应用将有很好的发展前景.     

反射式血氧饱和度检测系统研制

反射式血氧仪可以有效避免使用范围受限,但反射式脉搏血氧信号十分微弱,容易受噪声干扰使血氧参数的提取难度加大。为获得高质量有效的反射式容积脉搏波,系统设计中提出一种基于集成芯片AFE4490的脉搏血氧信号检测方案,对比几种基于小波变换的滤波算法去噪效果,完成对血氧信号较优的去噪处理。系统样机经调试后,基本实现反射方式对脉搏血氧饱和度的检测功能。     

具有人体活动情景辨识的穿戴式心电监测仪的研制

针对现有远程心电(ECG)监测设备在日常工作生活动态环境中应用存在的问题,本文根据信息融合思想,采用穿戴式技术,研制出了一种新型的具有人体活动情景辨识的穿戴式ECG监测仪,它能在检测人体ECG的同时,同步检测躯体运动的加速度信号,识别出人体的活动情景信息,最后通过融合ECG、活动情景信息来判断人体心脏的健康状况。本监测仪具有监测准确、使用方便、舒适、私密性好、可长期连续使用等突出优点,十分适合日常环境下人体心脏健康的监护。     

Artifacts in wearable photoplethysmographs during daily life motions and their reduction with least mean square based active noise cancellation method

Signal distortion of photoplethysmographs (PPGs) due to motion artifacts has been a limitation for developing real-time, wearable health monitoring devices. The artifacts in PPG signals are analyzed by comparing the frequency of the PPG with a reference pulse and daily life motions, including typing, writing, tapping, gesturing, walking, and running. Periodical motions in the range of pulse frequency, such as walking and running, cause motion artifacts. To reduce these artifacts in real-time devices, a least mean square based active noise cancellation method is applied to the accelerometer data. Experiments show that the proposed method recovers pulse from PPGs efficiently.     

Carbon nanotube-based self-adhesive polymer electrodes for wireless long-term recording of electrocardiogram signals

In this study, the concept of polymer electrodes integrated with a wireless electrocardiogram (ECG) system was described. Polymer electrodes for long-term ECG measurements were fabricated by loading high content of carbon nanotubes (CNTs) in polydimethylsiloxane. Silver nanoparticles (Ag NPs) were added to increase the flexibility of the polymer and the conductivity of the electrode. An ECG electrode patch was fabricated by integrating the electrodes with an adhesive polydimethylsiloxane (aPDMS) layer. Holes in the electrode filled with aPDMS can enable robust contact between the electrode and skin, reducing motion artifacts. A wireless ECG measurement system was developed and adapted to the polymer electrodes. The polymer electrodes combined with the measurement system were successfully applied in wireless, long-term recording of ECG signals. An eleven-day continuous test showed that the ECG signal did not degrade over time. The results of attach/detach tests demonstrated that the ECG signal was affected by motion artifacts after six attach/detach cycles. The electrodes produced are flexible and exhibit good ECG performance, and therefore can be used in wearable medical monitoring systems. The approach proposed in this study holds significant promise for commercial application in medical fields.     

蓝牙低功耗可穿戴血氧监测设备的设计

设计一种基于蓝牙低功耗技术的可穿戴血氧饱和度监测设备,用于实时、连续检测人体血氧饱和度和脉率。主要工作包括设计实现耳夹式光电传感器、太阳能电池插接件以及蓝牙模块等核心部件。设备和硬件设计采用低功耗元件及模块,数据通过低功耗蓝牙技术传至手机App,软件设计优化数据发送策略,具有低功耗、可穿戴、稳定可靠等特点,适合户外运动或者缺氧性疾病的血氧监测。测试表明,设备蓝牙通信误码率最终控制为0,脉率精度高达98.0%,当模拟仪输出血氧饱和度大于75%时,设备的检测精度高达97.9%。此外,创新性地使用太阳能电池进行冗余供电,整机待机电流为11 μA,全功率工作时长为18 h以上,续航性能优于市面上主流的指夹式血氧仪。     

Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines

Abstract

The use of wearable recorders for long-term monitoring of physiological parameters has increased in the last few years. The ambulatory electrocardiogram (A-ECG) signals of five healthy subjects with four body movements or physical activities (PA)—left arm up down, right arm up down, waist twisting and walking—have been recorded using a wearable ECG recorder. The classification of these four PAs has been performed using neuro-fuzzy classifier (NFC) and support vector machines (SVM). The PA classification is based on the distinct, time-frequency features of the extracted motion artifacts contained in recorded A-ECG signals. The motion artifacts in A-ECG signals have been separated first by the discrete wavelet transform (DWT) and the time–frequency features of these motion artifacts have then been extracted using the Gabor transform. The Gabor energy feature vectors have been fed to the NFC and SVM classifiers. Both the classifiers have achieved a PA classification accuracy of over 95% for all subjects.     

Wearable electromyography measurement system using cable-free network system on conductive fabric

OBJECTIVE: To solve the complicated wires and battery maintenance problems in the application of wearable computing for biomedical monitoring, the electromyography (EMG) measurement system using conductive fabric for power supply and electric shield for noise reduction is proposed. MATERIAL AND METHODS: The basic cable-free network system using conductive fabric, named as "TextileNet" is developed. The conductive fabric has the function of electric shield for noise reduction in EMG measurement, and it enables the precise EMG measurement with wearable system. RESULTS: The specifications of the developed prototype TextileNet system using wear with conductive fabric were communication speed of 9600bit/s and power supply of 3W for each device. The electric shield effect was evaluated for precise EMG measurement, and the shield efficacy of conductive fabric was estimated as high as that of shield room. CONCLUSIONS: TextileNet system solves both the problems of complicated wires and battery maintenance in wearable computing systems. Conductive fabric used in TextileNet system is also effective for precise EMG measurement as electric shield. The combination of TextileNet system and EMG measurement device will implement the cable-free, battery-free wearable EMG measurement system.     

Motion correction for three-dimensional chemical exchange saturation transfer imaging without direct water saturation artifacts

In chemical exchange saturation transfer (CEST) MRI, motion correction is compromised by the drastically changing image contrast at different frequency offsets, particularly at the direct water saturation. In this study, a simple extension for conventional image registration algorithms is proposed, enabling robust and accurate motion correction of CEST-MRI data. The proposed method uses weighted averaging of motion parameters from a conventional rigid image registration to identify and mitigate erroneously misaligned images. Functionality of the proposed method was verified by ground truth datasets generated from 10 three-dimensional in vivo measurements at 3 T with simulated realistic random rigid motion patterns and noise. Performance was assessed using two different criteria: the maximum image misalignment as a measure for the robustness against direct water saturation artifacts, and the spectral error as a measure of the overall accuracy. For both criteria, the proposed method achieved the best scores compared with two motion-correction algorithms specifically developed to handle the varying contrasts in CEST-MRI. Compared with a straightforward linear interpolation of the motion parameters at frequency offsets close to the direct water saturation, the proposed method offers better performance in the absence of artifacts. The proposed method for motion correction in CEST-MRI allows identification and mitigation of direct water saturation artifacts that occur with conventional image registration algorithms. The resulting improved robustness and accuracy enable reliable motion correction, which is particularly crucial for an automated and carefree evaluation of spectral CEST-MRI data, e.g., for large patient cohorts or in clinical routines.     

磁共振成像PROPELLER采样数据重建中的运动估计新算法

PROPELLER（推进器）采样技术能够利用K空间中心重叠采样区域的数据来估计采集过程中受检查者的运动进而加以补偿,对运动伪影的消除效果非常显著。然而,由于其重建时的运动估计是基于最大化频域空间上相关系数的配准算法,该算法为了实现旋转估计与平移估计的分离,在进行旋转估计时,仅仅采用K空间数据的模,在数据量有限的情况下造成估计精度较低,在重建图像上表现为模糊及星条状伪影。本研究基于最大化图像空间上的互信息提出一种PROPELLER采样数据的运动估计新算法,首先由每个K空间带进行傅立叶逆变换后取模重建出系列临时图像,对这些图像进行模糊增强后以互信息作为相似性测度迭代搜索最优的运动参数。实验证明,该方法能显著提高PROPELLER采样数据重建中运动估计与补偿的精度,从而更好地消除伪影,特别是用于有运动时T1加权头部成像时。     

Detection and deletion of motion artifacts in electrogastrogram using feature analysis and neural networks

Electrogastrogram is a surface measurement of gastric myoelectrical activity, and electrogastrography has been an attractive method for physiological and pathophysiological studies of the stomach due to its nonivasive nature. Motion artifacts, however, ruin the electrogastrogram (EGG), and make the analysis very difficult and sometimes even impossible. They must be eliminated from EGG signals before analysis. Up to now, this can only be done by visual inspection, which is not only time-consuming but also subjective. In this study, a method using feature analysis and neural networks has been developed to realize automatic detection and elimination of the motion artifacts in EGG recordings by computer. Experiments were conducted to investigate the characteristics of different motion artifacts. Useful features were extracted, and different combinations of the features used as the input of the neural network were compared to obtain the optimal performance for the detection of motion artifacts using the artificial neural network.     

Comparison of adaptive filtering in time-, transform- and frequency-domain: An electrogastrographic study

Adaptive cancellation of motion artifacts in the electrogastrogram (EGG) is presented in this paper. The EGG is a surface measurement of gastric electrical activity. Like other noninvasive electrophysiological measurements, the EGG contains motion artifacts. A number of papers have been published on the adaptive cancellation of motion artifacts or interferences in biomedical signals. Adaptive filtering was performed in time domain in almost all of the previous publications. In this paper, however, three different sorts of adaptive filters were investigated and their efficiencies in cancellation of motion artifacts were compared with each other. These include time-domain, transform-domain, and frequency-domain adaptive filters. A series of simulations were conducted to investigate the performance of these adaptive filters in cancellation of respiratory and motion artifacts. The results show that the frequency-domain adaptive filter has superior performance over the time- and transform-domain adaptive filters in the cancellation of stationary respiratory artifacts in the EGG. Although results focus on the EGG, this paper provides useful information for adaptive filtering of other biomedical signals.     

Derivation of Centers and Axes of Rotation for Wrist and Fingers in a Hand Kinematic Model: Methods and Reliability Results

In the field of 3D reconstruction of human motion from video, model-based techniques have been proposed to increase the estimation accuracy and the degree of automation. The feasibility of this approach is strictly connected with the adopted biomechanical model. Particularly, the representation of the kinematic chain and the assessment of the corresponding parameters play a relevant role for the success of the motion assessment. In this paper, the focus is on the determination of the kinematic parameters of a general hand skeleton model using surface measurements. A novel method that integrates nonrigid sphere fitting and evolutionary optimization is proposed to estimate the centers and the functional axes of rotation of the skeletal joints. The reliability of the technique is tested using real movement data and simulated motions with known ground truth 3D measurement noise and different ranges of motion (RoM). With respect to standard nonrigid sphere fitting techniques, the proposed method performs 10–50% better in the best condition (very low noise and wide RoM) and over 100% better with physiological artifacts and RoM. Repeatability in the range of a couple of millimeters, on the localization of the centers of rotation, and in the range of one degree, on the axis directions is obtained from real data experiments.