基于SOPC的复合式生理信号检测系统设计

设计完成了一种多生物电信号采集能力并能完成生物电信号模式识别和辅助诊断的复合式生物电信号检测系统。系统通过具备双通道的低噪声高共模抑制比的前置采集放大电路,可实现心电信号和表面肌电信号两种体表生物电信号的检测。通过FPGA硬件化实现的小波分解模块和在NiosⅡ软核中实现的FFT和BP神经网络算法,可以完成对采集到的心电信号心率监测、QRS波群的检测和ST段形态识别反馈监护者的健康信息;并通过提取表面肌电信号活跃段数据和时频域参数为运动性肌肉疲劳评估提供参考。系统通过LCD屏、音频输出和SD卡存储能够完成对信号实时波形和监护参数显示、报警输出和长时间监护数据的存储。     

基于USB接口的远程安卓手机心电测量系统的设计

为了方便测量心电信号,设计了一种便携的基于USB接口的远程安卓手机心电测量系统;系统由检测模块、USB接口模块、安卓手机端软件组成;检测模块检测心电信号,并通过USB接口把信号发送到手机上,手机上的软件对接收到的数据滤波处理,还原出心电波形图,并计算心率;在心率异常时,软件可以自动发送波形截图到指定邮箱和手机;经测试,系统测量精度较高,工作稳定。     

基于ARM和WIFI技术的心电信号实时检测系统设计与研究

当前心脏疾病是引发我国人民死亡的头号杀手,而传统的心电信号采集系统及终端存在检测数据遗漏、成本高以及适用人群范围窄等问题;该文依据模块化、低能耗、高性能的原则,设计了一种基于ARM和WIFI技术的心电信号实时采集系统;给出了心电信号实时检测系统各功能模块的介绍以及功能实现流程,上位机使用TCP Client作为客户端,MCU作为下位机端,并通过WIFI技术作为通信媒介,设计了主控模块、心电心率采集模块、LCD显示模块以及通信模块,实现了对心电信号的实时采集与传输;系统测试结果验证了该心电信号实时检测系统的实用性、合理性及有效性,实验结果达到预期目标,能够有效提高检测结果准确性,为发现和防治心脏疾病提供一种实时、可靠的检测平台。     

基于STM32便携式心电监护装置的设计

综合运用传感器技术、嵌入式技术和无线通信技术,设计了一种以STM32F103C8T6微处理器为核心的家用便携式心电监护装置.利用Pulse Sensor脉搏心率传感器采集心电信号,经STM32内置的模数转换器(ADC)处理得到数字信号.LCD屏用于实时显示心率值和心电图(ECG),心率数据通过蓝牙通信模块实时传输到手机...     

基于LabVIEW多功能虚拟仪器的研制及应用

研究设计一种基于LabVIEW的多功能虚拟仪器.该仪器具有信号检测、处理、存取、网络通讯和信号发生等功能,并与A3000过程控制实验系统和AEDK-LabACT教学实验装置进行连接,实现远程液位测量及电信号检测.     

电子技术在温度传感器中的应用

<正> 近年来,随着电子技术的发展和进步,温度传感器的种类正在不断增加。一般说,温度计可分为传感器和指示计。传感器是一种能把检测出的温度这一物理量转换为相应的电信号或压力信号输出的装置;指示计则是一种能把传感器的检测信号作为温度值来表示的装置。此外,如玻璃温度计,它是一种属于传感器和指示计一体化的装置。这里只介绍能把温度值作为电信号处理的温度传感器。     

一种表面肌电信号的无线采集方式

表面肌电信号包含了大量表征肌肉生物特征的信息,它是一种微弱的生物电信号且极易受多种噪声干扰。可利用银电极的特性设计一种检测表面肌电信号的电极,电极贴于人体皮肤表面对检测到的信号进行放大、滤波,基于STM32对检测到的信号进行数模转换并通过蓝牙4.0发送至其他控制设备,实现表面肌电信号的无创采集和无线传输。实验表明,该采集方式可检测到明显的表面肌电信号变化,并成功实现蓝牙之间数据的发送和接收。     

基于自适应滤波的可穿戴式心电信号检测系统

为了检测公安、消防官兵等高危职业人群的心电信号,以胸带作为穿戴载体,设计一款可穿戴式无线心电检测系统,在智能手机上实现心电、心率的实时传输与显示。考虑到警员日常的活动,基于自适应滤波器原理,将三轴加速度传感器作为参考信号,对比两种自适应滤波算法滤出运动伪迹(Motion Artifact,MA)后的输出波形。结果表明,采用归一化的最小均方算法(Normalized Least Mean Square,NLMS)的自适应滤波器输出心电信号基线平稳且R波定位准确性达99%以上。在正常的人体活动中实时测量的心率值误差在4%以内,心率测量精度较高。     

驾驶员疲劳状态生理信号的DFA

通过去趋势波动分析(DFA)研究驾驶员疲劳状态的心率变异性信号和脑电信号特征.22名被试在驾驶模拟器上进行模拟驾驶作业,采集驾驶过程中的心率变异性和脑电信号并进行离线分析.Wil-coxon符号秩检验用于分析清醒和疲劳两种状态间DFA标度指数的差异,受试者工作曲线(ROC)分析用于确定DFA区分疲劳驾驶和清醒驾驶的能力.结果表明:疲劳状态时,心率变异性和脑电信号的标度指数显著增加(Wilcoxon符号秩检验,p＜0.01);心率变异性和脑电信号标度指数的ROC下面积最大分别为0.75和0.78.DFA的标度指数具有应用于驾驶疲劳监测系统的前景.     

70通道EPR脑电信号采集系统的实现

首创一种新型的脑电检测技术。将多路ERP脑电信号分别经各自的前置放大电路、模/数转换电路进行采样和A/D转换,形成多路并行数据流,最后经数据汇集装置汇集成一路高速串行数据流,再经光纤介质传输到计算机进行分析处理。采用该技术的脑电检测装置可以满足对多路ERP脑电信号同时进行高速率(10 kHz每通道)、高精度(24 bit)采样的要求。软硬件测试表明,该装置具有稳定性好、抗干扰能力强、频带范围宽等特点。     

Mobile wearable device for long term monitoring of vital signs

In long-term prevention and in rehabilitation of health of elderly people the recording of vital signs plays an important role. Especially the progress of rehabilitation can be deduced from the recording of an electrocardigram (ECG), blood pressure and body temperature. In this paper we present a wireless coupled recording device for long-term monitoring of these vital sign signals. We record the ECG, the blood pressure and the skin temperature and include a 3D-acceleration sensor for the determination of the movements during recording. To deal with motion artifacts in all recorded properties we use data fusion to reject or correct distorted vital sign signals.     

Classification of electrocardiogram signals with support vector machines and extreme learning machine

An Electrocardiogram or ECG is an electrical recording of the heart and is used in the investigation of heart disease. This ECG can be classified as normal and abnormal signals. The classification of the ECG signals is presently performed with the support vector machine. The generalization performance of the SVM classifier is not sufficient for the correct classification of ECG signals. To overcome this problem, the ELM classifier is used which works by searching for the best value of the parameters that tune its discriminant function and upstream by looking for the best subset of features that feed the classifier. The experiments were conducted on the ECG data from the Physionet arrhythmia database to classify five kinds of abnormal waveforms and normal beats. In this paper, a thorough experimental study was done to show the superiority of the generalization capability of the Extreme Learning Machine (ELM) that is presented and compared with support vector machine (SVM) approach in the automatic classification of ECG beats. In particular, the sensitivity of the ELM classifier is tested and that is compared with SVM combined with two classifiers, and they are the k-nearest Neighbor Classifier and the radial basis function neural network classifier, with respect to the curse of dimensionality and the number of available training beats. The obtained results clearly confirm the superiority of the ELM approach as compared with traditional classifiers.     

Microprocessor-based portable system for heart condition diagnosis

Early diagnosis of heart disease is typically based on a cassette recording of the electrocardiogram (ECG) signal which is then studied and analysed using a microcomputer. The system is bulky, unreliable and prone to mechanical failure. This paper presents the design and implementation of a compact microprocessor-based portable system used for heart condition diagnosis over a long period. The system reads, stores and analyses the ECG signals repetitively in real time for a specified period. The diagnostic data and samples of ECG signals are stored throughout the test period. The system hardware and software design are oriented towards a single-chip microcomputer-based system, hence minimizing size. The operating algorithm is based on a logical approach to ECG signal diagnosis and hence requires little memory.     

便携式无线心电监护终端的设计

针对健康与亚健康人群对远程心电动态监护的需求,提出了一种可用于远程健康监护的便携式无线心电监护终端.此监护终端采用TI公司的超低功耗16bit单片机作为系统核心,集成了模拟心电信号处理电路,外部Flash存储模块与蓝牙通讯模块.该监护终端可完成心电信号的采集与心率实时计算,可保存1小时的心电数据,并可实现经手机与远程监护中心的实时通讯,依据监护中心的控制选择性上传实时心电数据或心率数据.实验结果表明,该监护终端可实现连续24小时的动态心电监护.     

基于IoT的可穿戴远程心电监测设备设计

为了实现人体心电信号的远程实时动态监测,设计了一款可穿戴心电监测系统。此设备由织物电极和控制盒组成,固定于紧身运动背心上,通过织物电极传感心电信号,蓝牙模块采样后,传输到手机APP端和主控制模块,主控制模块完成心率计算、阈值判断、心电压缩、数据存储和上报数据至物联网平台。该系统可以实现控制盒上报数据和蓝牙上传数据两种数据上传方式,具有功耗低、可靠性高、适用于日常生活等特点,具有较高的实用价值。     

不可逆电消融手术中心电同步算法及系统设计

随着不可逆电脉冲穿孔消融手术等疗法的发展,传统的心电信号同步设备从精确度、抗干扰能力以及安全性的角度无法满足要求。针对此问题,提出了适用于不可逆电消融手术的心电同步算法,包括一种区间可预测性条件和一种消融区间起点精确标定算法。在满足可预测条件的情况下对消融区间起点进行精确标定,实现在强干扰条件下的心电信号同步。经过实时心电信号和MIT-BIH心律失常数据库的验证,表明系统和算法具有较高的精度和可靠性,并可在嵌入式系统上实现。     

可穿戴心电信号采集与分析系统的设计与实现

针对传统心电采集设备的移动限制性以及佩戴的不舒适性,根据可穿戴计算特点,设计并实现了穿戴式心电采集与分析系统。系统采用自主研发的12/单导联心电采集模块进行心电信号采集,数据可存于采集设备或经3G网络传输到服务器端,同时所开发的软件可对心电图进行辅助病情分析,实现对佩戴人的心电监护。还研究并制作了插入式电极和织物电极,并通过二者的结合提高了采集心电信号的质量。实际佩戴和使用结果表明,使用插入式织物电极的可穿戴式心电采集设备具有良好的舒适性,心电信号波形的质量能够达到临床监控的要求。     

WebECG: A novel ECG simulator based on MATLAB Web Figure

Electrocardiogram is a signal containing information about the condition and operation of heart. Nowadays, many heart diseases can be efficiently diagnosed using these signals. However, a proper recognition and classification of the heart signals are essential requirement for the diagnosis of heart diseases. In this study, emphasizing on this requirement, a new ECG simulator based on MATLAB Web Figure called WebECG is designed and implemented to facilitate the education on ECG signals. Advanced flexibility and good visualization capabilities including 3-dimension view, zoom and move on ECG graphics are provided by WebECG. The users are able to plot ECG signals with different parameters, to plot the ECGs of nine arrhythmia types. Furthermore, WebECG is capable to add three different noises to ECG and to plot/analyze long-term ECGs. These properties of the WebECG support efficient web-based education of ECG signals.