基于单片机的多通道温度采集系统设计

设计了一种基于MSP430单片机的多通道温度采集系统。利用MSP430单片机的12位A/D转换器可以定时循环对多路被测温度点的温度进行转换、保存并可以通过通信将数据传输给上位机。系统中设置了LCD显示模块及行列式键盘,可以方便的实现人机交互。该系统可以作为集散控制系统中的温度采集终端,对工业生产中需要检测的多个分散温度点进行检测并通过通信传输给上位机实现集中处理。     

基于MSP430F169的深海环境数据采集系统设计

为解决深海环境信息采集问题.介绍一种基于MSP430F169的数据采集系统.本系统可实现对温度、盐度、压力等信息的采集和存储.并可上传至上位机.通过人机界面进行动态曲线显示、数据分析.系统采样精度高.功耗低.一次充电可长期工作.并给出以MSP430F169为核心的硬件电路图和关键源程序.经过海洋实验证明.此系统可以完成...     

基于MSP430单片机的多路数据采集系统的设计

介绍一种以MSP430单片机为核心的多路数据采集系统。系统由集成函数发生器ICL8038现场模拟产生一正弦波信号并通过LM331实现频率到电压的变换,从而供给单片机进行数据采集。系统采用单片机与上位机进行通讯,单片机负责对7路数据的采集、处理和控制显示,同时应答上位机命令。上位机面向用户,可以对系统进行控制,选择数据采集的方式。设计并实现了一种具有现场采集和显示并且采集方式可控制的多路数据采集器,该数据采集器具有硬件电路简单,采集精度较高,低功耗等特点,具有推广应用价值。     

基于FT245BM芯片和单片机串口通信的研究

介绍了以PC机为上位机,以单片机MSP430F169为下位机的串口通信。PC机上用软件编写界面,界面可以通过串口控制数据收发和数据数量。单片机控制温度传感器DS18B20采集数据并处理,经过接口芯片FT245BM将数据传送给PC机并在编写的界面中显示,同时PC机也可通过界面发送数据给单片机,进而控制单片机外围设备,从而实现上下位机的双向通信。     

基于MSP430和VB的温度监测系统设计

针对目前集群现场测温中人工采集的一些局限性,提出了一种基于MSP430的多点测温系统的设计方案.设计了MSP430单片机驱动DS18B20采集温度的硬件系统和软件系统以及利用VB编写上位机接收显示界面,定时刷新采集到的实时温度,通过折线图显示出温度变化趋势.结果表明,该系统能够准确采集各监测点的温度数据,并可以实时召测当前温度,为工程应用提供了重要依据.     

基于MSP430和USB的胎儿心电图仪的设计

介绍一种基于MSP430单片机和USB总线的胎儿心电图仪,并给出其硬件电路和软件设计方法。系统采用TI公司超低功耗单片机MSP430F149对母体腹部和胸部心电信号进行同步采集,并通过USB接口将数据发送到PC机,利用上位机软件完成处理、显示、存储等。系统性能可靠、使用方便、结构简单、成本低廉。     

基于SPCE061A处理器医疗模型人训练系统设计

医疗模型人训练系统是现代电脑技术与模拟技术相结合的一种交互式医学模拟教学产品,是集教学、培训、考核和实际操作于一体,知识全面、功能强大的计算机交互式模型人训练系统。该系统的下位机硬件采用凌阳16位单片机SPCE061A处理器用来采集模型人的生命体征参数以及控制模拟模型人的生理过程,上位机软件系统采用双屏显示的形式实现了控制模拟模型人的生理过程和监视模拟模型人的生命体征参数。该系统实现了在安全、受保护的超真实模拟环境里进行医学教学和训练。     

基于MS5534B的气压高度计系统的设计

采用Intersema公司基于MEMS的数字压力传感器MS5534B,利用海拔高度与气压和温度的关系,设计并制作了一个微型气压高度计。利用MSP430F449的I/O口模拟同步串行外设接口(SPI),实现了MSP430F449从数字压力测量模块MS5534B读取温度和气压数据的功能,经过相关的计算处理得到实际高度值,在液晶显示屏上实时显示温度和高度,并通过RS232通信接口把数据发回至上位机。设计的气压高度计样机具有质量小、功耗低、精度高等优点,经过实验测定,在标准大气压环境中其相对高度测量的平均标准偏差为0.2m,线性度为0.9999。     

基于MSP430单片机的工程机械故障诊断系统

阐述了工程机械故障诊断系统的设计方案,该系统包括运用MSP430单片机设计的故障诊断仪、运用SPComm控件设计的上位机与MSP430单片机之间的RS-232串行口通信系统以及基于Delphi7.0建立的上位机故障诊断软件系统,给出了故障诊断系统的实现方案,介绍了系统各模块的原理及程序框图。该设计方案解决了重型机械的故障诊断问题。实际应用中,性能可靠,效果良好。     

教室照明节能控制系统的智能化和可视化设计

针对目前高校教室照明日光灯不合理使用而造成的电力资源浪费问题,提出了由MSP430单片机、光频转换器和人体感应传感器组成的节能控制系统,通过合理安排时间以及综合处理人体和光强信号,来实现灯光的智能开、关,从而达到节能的智能化;通过C#编写的窗口程序和MSP430单片机的结合,将下位机上传的数据在上位机进行可视的实时监控。     

低噪声电子听诊器的设计

心音、呼吸音是人体重要的声音信号,是医生进行心脏和呼吸系统疾病诊断的重要信息依据。而一般的听诊器引入的噪声较大,影响了诊断的正确性。为实现提高听诊的准确度,需要设计一种低噪声电子听诊器电路。此设计主要包括低噪声的前置级放大电路、滤波电路以及功率放大电路。通过这种低噪声电子听诊器的电路设计,实现了对心音和呼吸音的低噪提取,为实现对心音和呼吸音的正确诊断奠定了良好的基础。     

Digital Spectrum Analysis of Respiratory Sound

The applcation of the fast Fourier transform in the digital analysis of various biological signals in the frequency domain is well-known. In this paper a method is described for spectrum analysis of respiratory sound using the technique of fast Fourier transform. Results of the application of the method to respiratory sounds of six normal subjects and six tuberculosis patients are presented. Such studies may well prove to be a promising noninvasive diagnostic tool for lung diseases and increase the clinical importance of lung auscultation.     

Classification of normal/abnormal PCG recordings using a time–frequency approach

The early and accurate diagnosis of cardiovascular diseases (CVDs) are of great importance as they allow early and proper medical treatment and therefore result in reducing the chance of the CVDs being developed to an acute level. In medical procedures, the first step in examining the cardiovascular function is the auscultation of the heart. However, the correct medical diagnosis based on the heart sounds through a stethoscope requires a lot of expertise and, in some cases, needs referral of the patient to a cardiologist. This is not only time-consuming but also imposes a financial burden on the medical system. Thus, automated detection and analysis of the recorded heart sound auscultation has received a lot of attentions in recent years. This study presents a new time–frequency (T–F) based approach for classifying phonocardiogram (PCG) signals into normal and abnormal. In the proposed methodology, each PCG recording is first segmented into the 4 fundamental heart cycles, i.e. S1, systole, S2, and diastole. From each state, a set of T–F features are extracted with the aim of identifying their characteristics in the T–F domain. The features are then applied to a support vector machine to classify the PCG signal into normal or abnormal. The performance of the proposed method is evaluated using the 2016 PhysioNet challenge database and compared with that of the best performing existing methods. The experimental results using tenfold cross-validation show that the proposed method outperforms the existing methods in terms of sensitivity, specificity, and accuracy.

     

基于心动周期估计的心音分割及异常心音筛查算法

心脏疾病是全球发病率和死亡率最高的疾病,心音听诊可以获取心脏的机械特性及结构特征,与超声心动图、核磁共振等无创诊断技术相比具有快速、低成本和操作简单的优势。心音信号成分复杂,容易受到各种噪声和干扰的影响,听诊诊断结果容易受到医生主观性的影响,极大限制了心音听诊的应用。该文提出一种基于心动周期估计的心音分割及异常心音筛查算法,预先估计了心音的心动周期,存在随机干扰的情况下也可以正确识别信号中80%以上的心动周期,提高了算法的稳定性。同时提出了区分度良好的时域和频域特征指标,利用支持向量机建模,对异常心音的识别率可达92%。算法可辅助医生诊断,或用于家用便携式心音监护设备。     

基于听诊音频谱分析的数字听诊系统设计

为获得定量化的听诊结果,设计了一种基于听诊音频谱分析的数字听诊系统。该数字听诊系统由硅麦克风传感器接收人体器官发出的声音,经过运算放大、滤波和模数转换,将模拟信号转换为数字信号传送至专用集成电路芯片进行后续处理,再由终端设备输出详细、准确的听诊音频谱分析结果,达到帮助精确记录和科学分析听诊结果的目的。     

A framework for the analysis of acoustical cardiac signals

Skilled cardiologists perform cardiac auscultation, acquiring and interpreting heart sounds, by implicitly carrying out a sequence of steps. These include discarding clinically irrelevant beats, selectively tuning in to particular frequencies and aggregating information across time to make a diagnosis. In this paper, we formalize a series of analytical stages for processing heart sounds, propose algorithms to enable computers to approximate these steps, and investigate the effectiveness of each step in extracting relevant information from actual patient data. Through such reasoning, we provide insight into the relative difficulty of the various tasks involved in the accurate interpretation of heart sounds. We also evaluate the contribution of each analytical stage in the overall assessment of patients. We expect our framework and associated software to be useful to educators wanting to teach cardiac auscultation, and to primary care physicians, who can benefit from presentation tools for computer-assisted diagnosis of cardiac disorders. Researchers may also employ the comprehensive processing provided by our framework to develop more powerful, fully automated auscultation applications.     

Low-Intensity Sensitive and High Stability Flexible Heart Sound Sensor Enabled by Hybrid Near-Field/Far-Field Electrospinning

Low-cost and wearable heart sound sensors can facilitate early detection and monitoring of cardiovascular and respiratory diseases. Such sensors are currently limited by either the complexity of fabrication processes or low sensitivity and reliability for weak signal detection. Here, a hybrid near-field/far-field electrospinning approach is demonstrated that enables low-cost fabrication and optimization of triboelectric heterostructures for heart sound sensing. Specifically, by combining the far-field produced highly polarized and porous polyvinylidene difluoride network for triboelectric electrification and near-field patterned polyurethane grid spacers for vibration enhancement and charge trapping, the greatly improved sensor output at the heart sound frequency (50–150 Hz) and intensity (<80 dB) range, demonstrating record high sensitivity of 7027 mV Pa⁻¹ and low detection limit of 47 dB. The sensor exhibits excellent stability under both sudden physical disturbance and long-term cycling stress, showing no degradation during 7 h of continuous operation. It is demonstrated that the sensor can be integrated with apparel and capture high-quality heart sound signals at all five diagnostic auscultation points and distinguish characteristic heart sound patterns caused by different cardiovascular diseases.     

Detection of Heart Valves Closure Instants in Phonocardiogram Signals

Wireless Personal Communications - Phonocardiogram based auscultation is the most suitable cardiac examination technique for primary health care since heart sound can be captured and analyzed using...     

Optimal wavelet denoising for phonocardiograms

Phonocardiograms (PCGs), recordings of heart sounds, have many advantages over traditional auscultation in that they may be replayed and analysed for spectral and frequency information. PCG is not a widely used diagnostic tool as it could be. One of the major problems with PCG is noise corruption. Many sources of noise may pollute a PCG including foetal breath sounds if the subject is pregnant, lung and breath sounds, environmental noise and noise from contact between the recording device and the skin. An electronic stethoscope is used to record heart sounds and the problem of extracting noise from the signal is addressed via the use of wavelets and averaging. Using the discrete wavelet transform, the signal is decomposed. Due to the efficient decomposition of heart signals, their wavelet coefficients tend to be much larger than those due to noise. Thus, coefficients below a certain level are regarded as noise and are thresholded out. The signal can then be reconstructed without significant loss of information in the signal content. The questions that this study attempts to answer are which wavelet families, levels of decomposition, and thresholding techniques best remove the noise in a PCG. The use of averaging in combination with wavelet denoising is also addressed. Possible applications of the Hilbert transform to heart sound analysis are discussed.     

心脏量化听诊的新方法研究

本文从时频两域研究心脏量化听诊的新方法,实现心脏的诊断从定性到定量转变,即从时、频两域分别提取蕴含着丰富临床评价信息的心音特征波形,并依据所设定的阈值计算特征诊断参数,这些特征参数配以易于理解的图解和数字化参数来快速准确地判别正常心音与各种心脏杂音,该技术的实现不仅有利于缓解医生长时间听诊造成的听力疲劳,也使得其有可能成为一种大规模的人群心脏状况普查方法。