Electrocardiogram signal processing method for exact Heart Rate detection
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现代电子技术Modern Electronics TechniqueJun.2023Vol.46No.122023年6月15日第46卷第12期0引言心电信号是人体活动的基本表征,是判断心律失常、心肌梗死等的重要临床依据。
而监测得到的信号常常存在各种噪声[1],从监测信号中提取有用信号是当前科研活动的关键。
目前提取有用信号多采用集合经验模态分解(EEMD )去噪处理手段,但此方法容易产生模态混叠现象,且对含噪声的固有模态IMF 分量直接剔除[2⁃5]等缺点会影响去噪性能。
对此,本文提出将改进的小波阈值与EEMD 相结合,应用于心电信号去噪。
首先介绍了改进的小波阈值方法和EEMD 算法;再将结合算法应用于模拟信号去噪,达到了良好效果;最后对真实心电信号进行去噪实验,实验证明了改进的算法是有效的,比单一的EEMD 算法应用于心电信号提取处理更有效。
1EEMD 算法介绍为解决经典EMD 方法模态混叠问题,研究者们提出了一种新的噪声辅助数据分析方法,即EEMD 算法。
EEMD 算法的核心思想是利用白噪声具有频率均匀分DOI :10.16652/j.issn.1004⁃373x.2023.12.023引用格式:蔡帮贵,朱雨男,王彪.EEMD 与改进小波阈值结合应用于心电信号去噪研究[J].现代电子技术,2023,46(12):137⁃140.EEMD 与改进小波阈值结合应用于心电信号去噪研究蔡帮贵1,2,朱雨男2,王彪2(1.四川卫生康复职业学院,四川自贡643000;2.江苏科技大学海洋学院,江苏镇江212000)摘要:心电信号监测过程中噪声是不可避免的,目前主要采用EEMD 算法对观测信号所带有的噪声进行滤除,但该方法会直接丢弃高频噪声主导的低阶IMF 分量或低频噪声主导的余项,导致部分有用信息丢失。
为此,文中提出将改进的小波阈值与EEMD 相结合,应用于心电信号去噪。
改进的阈值方法能有效地去除各IMF 分量噪声,再将处理后的各分量叠加,得到去噪的心电信号。
医疗器械英文缩写对照Medical Device Abbreviations in EnglishIntroduction:In the field of healthcare, medical devices play a crucial role in diagnosing, treating, and monitoring patients. With the advancement of technology, medical devices have become more sophisticated, leading to the need for standardized abbreviations in English. These abbreviations help professionals in the industry communicate effectively and efficiently. In this article, we will explore some commonly used medical device abbreviations and their corresponding meanings.I. Diagnostic Medical Devices:1. ECG - Electrocardiogram:The ECG is a diagnostic medical device used to measure the electrical activity of the heart. It is often used to diagnose various heart conditions such as arrhythmias, myocardial infarctions, and heart blockages.2. CT - Computed Tomography:CT scans utilize X-ray technology and computer processing to create detailed cross-sectional images of the body. This medical device allows healthcare professionals to detect abnormalities in organs, bones, and tissues.3. MRI - Magnetic Resonance Imaging:MRI is a non-invasive medical device that uses a powerful magnetic field and radio waves to generate detailed images of the body's internalstructures. It is particularly effective in diagnosing conditions involving the brain, spine, joints, and soft tissues.II. Therapeutic Medical Devices:1. CPAP - Continuous Positive Airway Pressure:CPAP machines are primarily used to treat sleep apnea, a condition characterized by interrupted breathing during sleep. This device delivers a continuous flow of pressurized air through a mask, keeping the airway open and improving the quality of sleep.2. PACemaker - Permanent Pacemaker:A pacemaker is a small, implantable device that helps regulate the heart's rhythm. It sends electrical impulses to the heart muscles, ensuring proper heart rate and rhythm in individuals with irregular heartbeats or bradycardia.3. TENS - Transcutaneous Electrical Nerve Stimulation:TENS units are used for pain management by sending electrical impulses through the skin. This device helps alleviate chronic and acute pain conditions, such as arthritis, back pain, and muscle strains.III. Monitoring Medical Devices:1. BPM - Blood Pressure Monitor:Blood pressure monitors measure the pressure exerted by circulating blood against the walls of blood vessels. This device provides essential information about a person's cardiovascular health and is commonly used both in clinical settings and for personal use at home.2. SpO2 - Blood Oxygen Saturation Monitor:SpO2 monitors, often referred to as pulse oximeters, measure the oxygen saturation level in a person's blood. This device is commonly placed on the fingertip and is widely used to monitor oxygen levels, particularly in individuals with respiratory conditions.3. EEG - Electroencephalogram:EEG machines record the electrical activity of the brain by placing electrodes on the scalp. This device is used to diagnose neurological disorders, such as epilepsy, sleep disorders, and brain tumors.IV. Surgical and Therapeutic Devices:1. LASER - Light Amplification by Stimulated Emission of Radiation:LASER devices produce an intense, focused beam of light that is used in various surgical and therapeutic procedures. LASERs are commonly used in eye surgeries, dermatology treatments, and laser hair removal.2. PPE - Personal Protective Equipment:PPE refers to a range of protective equipment designed to minimize the risk of exposure to hazardous substances or environments during medical procedures. Examples include gloves, face masks, gowns, and goggles.Conclusion:Accurate communication in the healthcare industry is of utmost importance. Standardized medical device abbreviations in English facilitate clear and concise communication among healthcare professionals. Understanding and correctly using these abbreviations can improveefficiency, reduce errors, and ensure the safe and effective use of medical devices.。
电子质量2020年第09期(总第402期)作者简介院赵章红(1968-),男,硕士研究生,研究员,高级经济师,研究方向为虚拟仿真、大数据、人工智能、计算机应用、临床医学。
一种医学动物实验心电信号采集设计方法A Design Method of ECG Signal Acquisition in Medical Animal Experiment赵章红1,杨阳2,张样平1,王扎根1(1.河南恒茂创远科技股份有限公司,河南郑州450000;2.河南省大数据中心,河南郑州450000)Zhao Zhang-hong 1,Yang Yang 2,Zhang Yang-ping 1,Wang Zha-gen 1(1.Henan Hengmao Chuangyuan Technology Co.,Ltd.,Henan Zhengzhou 450000;2.Henan big data center,Henan Zhengzhou 450000)摘要:该文主要介绍了一种用于医学动物实验心电采集的设计方法,包括主控模块、心电采集模块、电源模块、通信模块四个部分。
阐述了心电采集的硬件系统组成和软件设计,讲述了该心电采集仪如何采集数据、显示波形、分析波形数据及前景展望等内容。
解决了在医学动物心电信号采集仪器昂贵、操作不简便等问题。
关键词:心电(ECG)信号;STM32F103C8T6;ADS1292;去噪处理;工频干扰中图分类号:TN911.7;TP274+.2文献标识码:A文章编号:1003-0107(2020)09-0022-05Abstract:This paper mainly introduces a design method for ECG acquisition in medical animal experiments.It includes four parts:main control module,ECG acquisition module,power supply module and communication module.This paper expounds the hardware system composition and software design of ECG acquisition,and describes how to collect data,display waveform,analyze waveform data and prospect of the ECG acquisition instrument.The problems such as the excessive high price,difficult operation of ECG signal acquisition instru-ment in medical animals and so on were solved.Key words:electrocardiogram signal;STM32F103C8T6;ADS1292;denoising processing;power frequency in-terferenceCLC number:TN911.7;TP274+.2Document code:AArticle ID :1003-0107(2020)09-0022-050引言心电信号作为医学动物实验中一项重要的生物信号,也是研究心血管疾病的重要指标。
专业术语缩略语英文全称中文全称ADE Adverse Drug Event 药物不良事件ADR Adverse Drug Reaction 药物不良反应AE Adverse Event 不良事件AI Assistant Investigator 助理研究者BMI Body Mass Index 体质指数CI Co—investigator 合作研究者COI Coordinating Investigator 协调研究者CRA Clinical Research Associate临床监查员(临床监察员)CRC Clinical Research Coordinator 临床研究协调者CRF Case Report Form 病历报告表CRO Contract Research Organization 合同研究组织ECRF 电子化病历报告表CSA Clinical Study Application 临床研究申请CTA Clinical Trial Application 临床试验申请CTX Clinical Trial Exemption 临床试验免责CTP Clinical Trial Protocol 临床试验方案CTR Clinical Trial Report 临床试验报告DSMB Data Safety and monitoring Board 数据安全及监控委员会EDC Electronic Data Capture 电子数据采集系统EDP Electronic Data Processing 电子数据处理系统FDA Food and Drug Administration 美国食品与药品管理局FR Final Report 总结报告GCP Good Clinical Practice 药物临床试验质量管理规范GLP Good Laboratory Practice 药物非临床试验质量管理规GMP Good Manufacturing Practice 药品生产质量管理规范IB Investigator’s Brochure研究者手册IC Informed Consent 知情同意ICF Informed Consent Form 知情同意书ECGElectrocardiogram心电图ICH International Conference on Harmonization 国际协调会IDM Independent Data Monitoring 独立数据监察IDMC Independent Data Monitoring Committee 独立数据监察委员会IEC Independent Ethics Committee 独立伦理委员会IND Investigational New Drug 新药临床研究IRB Institutional Review Board 机构审查委员会IVD In Vitro Diagnostic 体外诊断MA Marketing Approval/Authorization 上市许可证IVRS Interactive Voice Response System 互动语音应答系统MCA Medicines Control Agency 英国药品监督局MHW Ministry of Health and Welfare 日本卫生福利部NDA New Drug Application 新药申请NEC New Drug Entity 新化学实体NIH National Institutes of Health 国家卫生研究所(美国) PI Principal Investigator 主要研究者PL Product License 产品许可证PMA Pre—market Approval (Application)上市前许可(申请)PSI Statisticians in the Pharmaceutical Industry 制药业统计学家协会QA Quality Assurance 质量保证QC Quality Control 质量控制RA Regulatory Authorities 监督管理部门SA Site Assessment 现场评估SAE Serious Adverse Event 严重不良事件SAP Statistical Analysis Plan 统计分析计划SAR Serious Adverse Reaction 严重不良反应SD Source Data/Document 原始数据/文件SD Subject Diary 受试者日记Subject identification code (SIC)受试者识别代码SFDA State Food and Drug Administration 国家食品药品监督管理局SDV Source Data Verification 原始数据核准SEL Subject Enrollment Log 受试者入选表SI Sub—investigator 助理研究者SI Sponsor—Investigator 申办研究者SIC Subject Identification Code 受试者识别代码pd pharmacodynamics药物效应动力学SOP Standard Operating Procedure 标准操作规程pkpharmacokinetics药物代谢动力学SPL Study Personnel List 研究人员名单SSL Subject Screening Log 受试者筛选表T&R Test and Reference Product 受试和参比试剂UAE Unexpected Adverse Event 预料外不良事件WHO World Health Organization 世界卫生组织Active Control 阳性对照、活性对照WHO-ICDRA WHO International Conference of Drug Regulatory Authorities WHO国际药品管理当局会议Unexpected adverse event (UAE)预料外不良事件Audit 稽查Audit Report 稽查报告Auditor 稽查员Blank Control 空白对照Blinding/masking 盲法/设盲Case History 病历Clinical study 临床研究Clinical Trial 临床试验Clinical Trial Report 临床试验报告Compliance 依从性Coordinating Committee 协调委员会Cross-over Study 交叉研究Double Blinding 双盲Endpoint Criteria/measurement 终点指标Essential Documentation 必需文件Exclusion Criteria 排除标准Inclusion Criteria 入选表准Information Gathering 信息收集Initial Meeting 启动会议Inspection 检察/视察Institution Inspection 机构检察Investigational Product 试验药物Investigator 研究者Monitor 监查员(监察员)Monitoring 监查(监察)Monitoring Plan 监查计划(监察计划)Monitoring Report 监查报告(监察报告)Multi-center Trial 多中心试验Non—clinical Study 非临床研究Original Medical Record 原始医疗记录Outcome Assessment 结果评价Patient File 病人档案Patient History 病历Placebo 安慰剂Placebo Control 安慰剂对照Preclinical Study 临床前研究Protocol 试验方案Protocol Amendments 修正案Randomization 随机Reference Product 参比制剂Sample Size 样本量、样本大小Seriousness 严重性Severity 严重程度Single Blinding 单盲Sponsor 申办者Study Audit 研究稽查Subject 受试者Subject Enrollment 受试者入选Subject Enrollment Log 受试者入选表Subject Identification Code List 受试者识别代码表Subject Recruitment 受试者招募Subject Screening Log 受试者筛选表System Audit 系统稽查Study Site 研究中心Test Product 受试制剂Trial Initial Meeting 试验启动会议Trial Master File 试验总档案Wash—out 洗脱Trial Objective 试验目的Triple Blinding 三盲Wash—out Period 洗脱期Alb白蛋白ALD(Approximate Lethal Dose)近似致死剂量ALP碱性磷酸酶Alpha spending function消耗函数ALT丙氨酸氨基转换酶Approval批准Analysis sets统计分析的数据集Approval批准ATR衰减全反射法Assistant investigator助理研究者AST天门冬酸氨基转换酶AUCss稳态血药浓度-时间曲线下面积Standard operating procedure (SOP)标准操作规程Case report form/ case record form(CRF)病例报告表病例记录表Clinical trial application (CTA)临床试验申请Clinical trial exemption (CTX)临床试验免责Clinical trial protocol (CTP)临床试验方案Contract research organization (CRO)合同研究组织Computer-assisted trial design (CATD)计算机辅助试验设计Source data (SD)原始数据Electronic data capture (EDC)电子数据采集系统Source data verification (SDV)原始数据核准Electronic data processing (EDP)电子数据处理系统Subject enrollment log受试者入选表Institution review board (IBR)机构审查委员会Intention—to –treat (ITT)意向性分析(-统计学)Interactive voice response system (IVRS)互动式语音应答系统Investigator’s brochure (IB)研究者手册Maximum Tolerated Dose (MTD)最大耐受剂量Principle investigator (PI)主要研究者Product license (PL)产品许可证Serious adverse event (SAE)严重不良事件Serious adverse reaction (SAR)严重不良反应。
心电图信号处理技术的发展和应用心电图信号处理技术(Electrocardiogram Signal Processing)是一种将生物信号采集和处理技术相结合的新型技术。
它是目前与智能医疗行业相结合的一项热门技术,该技术能够通过采集患者的心电信号,辅助医生对患者进行诊断,并及时预警和提醒患者的健康状况。
心电图信号处理技术的发展历程可追溯至上世纪二三十年代,当时科学家因为对心脏疾病的研究而开发了心电图(Electrocardiogram)这项技术,通过这项技术能够观察心脏的电活动并把这种电活动变化的情况记录下来。
这项技术不仅为医生提供了一种基础性的病理分析方法,还为现代心脏病学的研究和探索提供了便利条件。
在此基础上,心电图信号处理技术也在逐渐地发展和完善。
传统的心电图只能将信号记录下来,在测量后医生需要花费大量的时间进行分析,而一些低级别的心脏疾病的诊断会因无法准确分析而被忽略。
因此,开发出具有可视化和自动分析的心电图处理软件就显得尤为重要。
近些年来,随着人工智能技术的迅猛发展,特别是深度学习算法的应用,心电图信号处理技术的发展也得到了很大的推进。
人工智能技术能够分析巨大的心电图数据,通过深度学习算法学习到心电信号的规律,再结合临床数据,能够更好地解决某些疑难情况下的心脏病诊断。
目前,美国和欧洲的大型医院已经在临床应用中推广了心电图信号处理技术,取得了相当的成效。
如改变历史上心律失常的诊断方法,以前是通过人工查看心电图进行判断,现在已经厌倦了这一种方式,取而代之的是智能系统来帮助医生进行诊断,不仅极大了提高了工作效率,而且在准确性方面也相对得到了提升。
此外,在一些突发情况的处理方面,心电图信号处理技术也取得了一些成功。
例如,当患者出现心脏跳动过快或过慢的情况,为了防止心律失常而引发的生命危险,就需要立即对患者的心电图数据进行处理,及时地发现患者病情的变化并进行处理。
此外,为了更好地为患者提供定制化的看护方案,心电图信号处理技术也需要遵循“数据安全”原则,转换后的数据仅医疗人员才可查看和使用。
信息英语词汇(E)e type constant e型常数e/r model 实体联系模型eam 电动式会计机ear 耳机early failure 早期故障earom 电改写只读存储器earphone 耳机earpiece 耳机earth detector 漏电检测器earth resistance 接地电阻earth station 地球站ebcdic 扩充的二十进制交换码ebcdic address ebcdic 地址ebcdic character ebcdic字符ebcdic transparency ebcdic 透迷ebr 电子束记录ecb 事件控制块ecg 心电图echo 回波echo attenuation 反射衰减echo check 回送校验echo printing 回送打印echo sounding 回波测深echo suppressor 回波抑制器echoing 回送ecl 射极耦合逻辑edge connector 板边插头edge notch card 边缘穿孔卡片edge notched card 边缘切咔片edge perforated cord 边缘切口卡片edge punched card 边缘切咔片edge train 电路edit 编辑edit command 编辑命令edit directed transmission 编排式传输edit instruction 编辑指令edit mode 编辑方式edit routine 编辑程序edit session 编辑时间edit statement 编辑语句editing 编辑editing capability 编辑能力editing character 编辑字符editing function 编辑功能editing key 编辑键editing program 编辑程序editing statement 编辑语句editing symbol 编辑符号editing terminal 编辑终端editor 编辑程序edp 电子数据处理edp consultance 电子数据外理咨询edp department 电子数据外理部edp industry 电子数据外理工业edps 电子数据处理系统education for computers 计算机教育education of a computer 计算机教育eeg 脑电图eeprom 电可擦可编程序只读存储器effective address 有效地址effective bit 有效位effective byte 有效字节effective instruction 有效指令effective range 有效范围effective speed 有效速度effective time 有效时间effective value 实际值efficiency 效率efl 射极跟随七辑egoless programming 无自我程序设计eigenvalue 本盏eigenvector 本镇量eight bit byte 八位字节eight channel code 八单位码eight track punched tape 八单位穿孔带eight's complement 八进制补码either or 或运算either way circuit 半双工电路elapsed time 经时计时elapsed time clock 计时时钟electric calculating machine 电动计算机electric cash register 电动出纳机electric charge 电荷electric controller 电控制器供电控制设备electric current 电流electric delay line 电延迟线electric typewriter 电动打字机electrical accounting machine 电动式会计机electrical analogy 电模拟electrical balance 电平衡electrical sensing 电读出electrically alterable read only memory 电改写只读存储器electrically erasable programmable read only memory 电可擦可编程序只读存储器electrically operated valve 电动阀门electrocardiogram 心电图electrocardiograph 心电图描记器electrochemical recording 电化学记录electrodynamic instrument 电动仪表electroencephalogram 脑电图electromagnetic delay line 电磁延迟线electromagnetic screen 电磁屏蔽electron 电子electron beam recording 电子束记录electron conduction 电子导电electron gun 电子枪electron shell 电子壳electron tube tester 电子管试验机electronic accounting machine 电子会计机electronic beam 电子束electronic brain 电脑electronic calculator 电子计算器electronic cash 电子货币electronic components 电子元件electronic computer 电子计算机electronic data processing 电子数据处理electronic data processing machine 电子数据处理机electronic data processing system 电子数据处理系统electronic digital computer 电子数字计算机electronic document 电子文件electronic editor 电子编辑机electronic file cabinet 电子文件箱electronic instrument 电子仪器electronic journals 电子杂志electronic library 电子图书馆electronic magnitude 幅度electronic mail 电子邮件electronic mail service 电子邮件业务electronic memory 电子存储器electronic parallel digital computer 并行电子数字计算机electronic pen 电子笔electronic periodicals 电子期刊electronic printer 电子打印机electronic punch 电子穿孔机electronic punch card machine 电子穿孔卡片机electronic random number generator 电子随机数发生器electronic serial digital computer 串行电子数字计算机electronic switch 电子开关electronic switching system 电子开关系统electronic tube 电子管electronic x y recorder 电子xy记录器electronics 电子学electroscope 验电器electrosensitive paper 电灼印刷纸electrostatic memory 静电存储器electrostatic plotter 静电绘图机electrostatic printer 静电印刷机electrostatic screen 静电屏幕electrostatic storage 静电存储器electrostatic store 静电存储器electrothermal printer 电热式印刷机electrothermal recording 电热式记录element 元件element expression 元素表达式element variable 元素变量elementary function 初等函数elementary item 基本项elevation 标高eleven punch 第11穿孔位elimination factor 消除因子embedded computer 嵌入式计算机embedded database 嵌入式数据库embedded interpreter 嵌入式解释程序embedded loop 嵌入循环embedded pointer 嵌入指示器embedded procedures 嵌入过程embedded software 嵌入式软件embedding 嵌入emergency 紧急emergency button 应急按钮emergency cutout 紧急断开emergency maintenance 应急维修emergency protection 事故保护emergency pull switch 应急切断开关emergency repair 应急修理emergency switch 应急开关emission 发射emitter 发射极emitter coupled logic 射极耦合逻辑emitter follower 射极跟随器emitter follower logic 射极跟随七辑empirical formula 经验公式empty loop 空循环empty medium 空白媒体empty set 空集empty string 空串emulation 仿真emulator 仿真器enable 允许enable input 允许输入enable pulse 允许脉冲enabled interrupt 允许中断enabling signal 允许信号encapsulated type 封装类型encapsulation 密封enclosure 机壳encoder 编码器encoder matrix 编码矩阵encoding 编码encoding device 编码装置encoding system 编码系统encryption 加密encryption key 加密密钥end 端end around borrow 循环借位end around carry 循环进位end around shift 循环移位end device 终端装置end exchange 本地交换局end line 结束行end mark 结束标志end of address 地址结束end of block 块结束end of data 数据结尾end of file 文件结束end of file indicator 文件结束指示符end of file mark 文件结束标志end of file statement 数据文件结束语句end of job 椎结束end of medium character 媒体终端符end of message 信息结束end of reel 磁带卷尾end of run 运行结束end of tape 带结束end of tape marker 带结束标志end of tape routine 带结束例程end of text 正文结束end of text character 正文结束符end of transmission 传输结束end of transmission block 信息组传输结束end of transmission block character 信息组传输结束符end of transmission character 传输结束符end of volume 卷结束end office 本地交换局end point 终点end point control 终点控制end printing machine 末尾打印机end user 最终用户end user language 端点用户语言ending tape mark 带结束标志endless loop 无限循环endless tape 无端磁带energetics 能量学energy 能energy converter 能量变换器energy efficiency 能量效率energy generation 能量产生energy loss 能量损耗energy source 能源energy spectrum 能谱engaged signal 忙音engine analyzer 马达分析机engine room telegraph 机械式电报engineering 工程engineering solution 工程解engineering time 维护检修时间engineering workstation 工程工专enhancement 增强enhancement mos 增强型金氧半导体掐enq 询问enqueueing 入队enquiry 询问enquiry character 询问符enter key 结束键entering 进入enterprise 企业体enterprise database 企业数据库enterprise level 企业水平enthalpy 焓entity 实体entity identifier 实体标识符entity relation diagram 实体关系图entity relationship 实体关系entity relationship model 实体联系模型entity relationship relation 实体关系比entropy 平均信息量entry 进入entry address 入口地址entry approval 输入认可entry condition 入口条件entry configuration 起时设备配置entry definition group 项目定义组entry instruction 入口指令entry label 入口标号entry linkage 入口连接entry name 入口标号entry point 入口点entry point address 入口点地址entry record 输入记录entry symbol 入口符号entry time 入口时间enumeration 枚举enumeration type 枚举类型envelope 包络面envelope delay 群延迟enveloping surface 包络面environment 环境environment conditions 环境条件environment division 环境部分environment record 环境记录environmental contamination 环境污染environmental protection 环境保护environmental test 环境监测eoa 地址结束eob 块结束eof 文件结束eor 磁带卷尾eot 带结束eov 电动阀门epitaxy 晶体取向接长eprom 可擦可编程序只读存储器equality 等效equality circuit 一致性电路equalization 均衡equals sign 等号equation 方程式equation solver 方程解算机equipment 设备equipment check bit 设备检查位equipment compatibility 设备兼容性equipment failure 设备故障equivalence 等价equivalence class 等价类别equivalence element 重合元件equivalence relation 等值关系equivalent circuit 等效电路equivalent to element 重合元件erasable disk 可擦磁盘erasable programmable read only memory 可擦可编程序只读存储器erasable prom 可擦可编程序只读存储器erasable storage 可擦存储器erase character 删除字符erase head 清洗磁头erase key 清洗键erase pulse 清冼脉冲erasing 清除erasing head 清洗磁头erasing magnetic head 消磁头erasure 清除ergonomics 工效学erlang 厄兰erp 误差校正过程error 错误error analysis 差错处理error burst 错误段error byte 错误字节error character 错误字符error check 错误检验error checking code 检错码error checking program 错误检验程序error code 错误代码error condition 错误状态error control 错误控制error control character 错误控制字符error correct retry 纠错重执error correcting capabilities 纠错能力error correcting code 错误校正码error correcting compiler 误差校正编译程序error correction 错误校正error correction routine 纠错程序error curve 误差曲线error detecting code 错误检测码error detection 错误检测error detection routine 错误检测程序error diagnosis 错误诊断error diagnostics 错误诊断error display 误码显示error flag 误差标记error free operation 无错操作error frequency 差错频率error function 误差函数error handling 差错处理error interrupt 错误中断error latch 错误锁存器error latency 错误潜伏期error list 错误表error log 错误日志error logging 出错记录error management 错误管理error message 错误报文error of approximation 近似误差error probability 错误概率error propagation 误差传播error protection 出错防止error range 误差范围error rate 出错率error ratio 误错比error recovery 错误校正error recovery procedure 误差校正过程error routine 查错程序error signal 误差信号error span 误差跨度error status code 错误状态码error transfer function 误差传递函数esc 转义字符escape 转义escape character 转义字符escape code 转义码escape key 换码键escape language 转义语言escape sequence 换码顺序esd 外部符号字典ess 电子开关系统estimate 估计estimated performance 估计性能estimation problem 估计问题estimation value 估计值estimator 预估程序etb 信息组传输结束etb character 信息组传输结束符etching 腐蚀evaluation 评价evaluation function 估价函数evaluation test 鉴定试验even harmonic 偶次谐波even number 偶数even odd check 奇偶校验even parity 偶数奇偶性even parity check 偶数奇偶性校验event 事件event control block 事件控制块event flag 事件标记event input mode 事件输入方式event probability 事件概率event variable 事件变量evolution 开方evolutionary system 改良系统exact division 精确除法exactitude 准确性except circuit 禁止门except gate 禁止门exception handler 异常处理程序exception handling 异常处理exceptional condition 异常条件excess 64 code 余64码excess fifty code 余50码excess three code 余3表示法excessiveness 冗余exchange 电话交换exchange area 通话区exchange buffering 交换缓冲exchange device 交换装置exchange register 交换寄存器exchangeable disk 可换磁盘exchangeable store 可换存储器exchanging 交换exclusion 禁止运算exclusive access 排斥存取exclusive control 排斥控制exclusive disjunction 或运算exclusive mode 互斥方式exclusive nor gate 同门exclusive or 异exclusive or gate 异门exclusive or operation 异操作exclusive reference 互斥引用exclusive segments 互斥段exclusive tree 互斥树exec statement 执行语句executable file 执行文件executable instruction 可执行指令executable module 执行模块executable program 可执行程序executable statement 可执行语句execute instruction 执行指令execute part of cycle 周期的执行部分execute phase 执行阶段execute statement 执行语句execution 执行execution cycle 执行周期execution logging 执行程序记录execution path 执行通路execution phase 执行阶段execution time 执行时间executive 等程序executive address 执行地址executive control program 执行控制程序executive directive 执行程序得executive dump 执行转储executive form 执行型executive instruction 管理指令executive logging 执行程序记录executive mode 执行状态executive program 执行程序executive resident 执行驻留executive routine 执行程序executive statement 执行语句executive supervisor 执行管理程序executive system 执行系统executive system utility 执行系统应用程序exerciser 试验程序exhaustion of spares 冗余部分全用exhaustive method 穷举方法exhaustive search 穷举搜索完全搜索exigent condition 紧急状态existential quantifier 存在量词exit 出口exit conditions 出口条件exit instruction 退出指令exit linkage 出口连接exit point 出口点exit statement exit 语句exjunction 异exjunction gate 异门expandability 可扩充性expander 扩展器扩展电路expansion 扩充expansion board 扩充板expansion bus 扩充总线expansion in series 系列扩展expansion slot 扩充槽expectation value 望值expert knowledge 专家知识expert system 专家系统explainer 说锰序explanation facilities 解释设备explanation generator 解释生成程序explanation module 解释模抉explicit address 显式地址explicit declaration 显式说明explicit length 显式长度explicit translation 显式转换exploded view 分解图exploring coil 测试线圈exponent 阶exponent arithmetic 指数运算exponent form 指数型exponent overflow 阶码溢出exponent part 阶部分exponent sign 指数符号exponential distribution 指数分布exponential function 指数函数exponentiation 乘方export list 出口目录exposure 曝光expression 表达式expression statement 表达式语句expressiveness 可表达性extended addressing 扩充编址extended ascii 扩充美国信息交换标准码extended backus naur form 扩充巴科斯诺尔范式extended control mode 扩充控制方式extended mnemonic code 扩充助记码extended precision 扩展精度extended precision arithmetic 扩充精度运算extended precision number 扩充精度数extended precision word 扩充精度语extended time scale 扩展时标extender board 扩充板extensible addressing 可扩充寻址extensible language 可扩充语言extensible notation 可扩充的记数法extensible syntax 可扩充的语法extension 扩充extension chassis 扩充机壳extension register 扩充寄存器extent 扩充范围external characteristic 外特性external clocking 外部定时external command 外部命令external decimal number 外部十进制数external delay 外因延迟external device 外部设备external disturbance 外部干扰external environment 外部环境external equipment 外部设备external feedback 外反馈external file 外部文件external fragmentation 外部碎片external interrupt 外部中断external key 外部关键码external label 外部标号external mass storage 外部大容量存储器外部海量存储器external memory 外存储器external name 外部名external operating ratio 运行率external page address 外页面地址external page storage 外页面存储器external page table 外页表external performance 外部性能external procedure 外部过程external program 外部程序external reference 外部引用external representation 外部表示external residence 外部常驻external schema 外模式external sort 外分类external specification 外部说明external storage 外存储器external symbol 外部符号external symbol dictionary 外部符号字典extra accuracy 附加准确度extract instruction 抽取指令extraction 提取extractor 提取器extreme accuracy 极限准确度extremum control 极值控制extrinsic semiconductor 非本针导体。
电子与通信专业英语Digital Signal Processing (英文翻译)姓名:赵豪班级:信工 122学号:2012020217Digital Signal Processing1、IntroductionDigital signal processing (DSP) is concerned with the representation of th e signals by a sequence of numbers or symbols and the processing of these s ignals. Digital signal processing and analog signal processing are subfields of signal processing. DSP includes subfields like audio and speech signal proce ssing, sonar and radar signal processing, sensor array processing, spectral es timation, statistical signal processing, digital image processing, signal process ing for communications, biomedical signal processing, seismic data processin g, etc.Since the goal of DSP is usually to measure or filter continuous real-world analog signals, the first step is usually to convert the signal from an analog to a digital form, by using an analog to digital converter. Often, the required outp ut signal is another analog output signal, which requires a digital to analog co nverter. Even if this process is more complex than analog processing and has a discrete value range, the stability of digital signal processing thanks to error detection and correction and being less vulnerable to noise makes it advanta geous over analog signal processing for many, though not all, applications.DSP algorithms have long been run on standard computers, on specializ ed processors called digital signal processors (DSP)s, or on purpose-built har dware such as application-specific integrated circuit (ASICs). Today there areadditional technologies used for digital signal processing including more powe rful general purpose microprocessors, field-programmable gate arrays (FPGA s), digital signal controllers (mostly for industrial applications such as motor co ntrol), and stream processors, among others.In DSP, engineers usually study digital signals in one of the following do mains: time domain (one-dimensional signals), spatial domain (multidimensio nal signals), frequency domain, autocorrelation domain, and wavelet domains. They choose the domain in which to process a signal by making an informed guess (or by trying different possibilities) as to which domain best represents t he essential characteristics of the signal. A sequence of samples from a meas uring device produces a time or spatial domain representation, whereas a disc rete Fourier transform produces the frequency domain information that is the f requency spectrum. Autocorrelation is defined as the cross-correlation of the s ignal with itself over varying intervals of time or space.2、Signal SamplingWith the increasing use of computers the usage of and need for digital si gnal processing has increased. In order to use an analog signal on a compute r it must be digitized with an analog to digital converter (ADC). Sampling is us ually carried out in two stages, discretization and quantization. In the discretiz ation stage, the space of signals is partitioned into equivalence classes and q uantization is carried out by replace the signal with representative signal value s are approximated by values from a finite set.The Nyquist-Shannon sampling theorem states that a signal can be exact ly reconstructed from its samples if the samples if the sampling frequency is g reater than twice the highest frequency of the signal. In practice, the sampling frequency is often significantly more than twice the required bandwidth.A digital to analog converter (DAC) is used to convert the digital signal ba ck to analog signal.The use of a digital computer is a key ingredient in digital control systems .3、Time and Space DomainsThe most common processing approach in the time or space domain is e nhancement of the input signal through a method called filtering. Filtering gen erally consists of some transformation of a number of surrounding samples ar ound the current sample of the input or output signal. There are various ways to characterize filters, for example: A“linear” filter is a linear transformation of i nput samples; other filters are “non-linear.” Linear filters satisfy the superpositi on condition, i.e. if an input is a weighted linear combination of different signal s, the output is an equally weighted linear combination of the corresponding o utput signals.A “causal” filter uses only previous samples of the input or output signals; while a “non-causal” filter uses future input samples. A non-causal filter can u sually be changed into a causal filter by adding a delay to it.A“time-invariant” filter has constant properties over time; other filters suchas adaptive filters change in time.Some filters are “stable”, others are “unstable”. A stable filter produces an output that converges to a constant value with time, or remains bounded withi n a finite interval. An converges to a constant value with time, or remains bou nded within a finite interval. An unstable filter can produce an output that grow s without bounds, with bounded or even zero input.A“Finite Impulse Response” (FIR) filter uses only the input signal, while a n “Infinite Impulse Response” filter (IIR) uses both the input signal and previou s samples of the output signal. FIR filters are always stable, while IIR filters m ay be unstable.Most filters can be described in Z-domain (a superset of the frequency do main) by their transfer functions. A filter may also be described as a difference equation, a collection of zeroes and poles or, if it is an FIR filter, an impulse r esponse or step response. The output of an FIR filter to any given input may b e calculated by convolving the input signal with the impulse response. Filters c an also be represented by block diagrams which can then be used to derive a sample processing algorithm to implement the filter using hardware instruction s.4、Frequency DomainSignals are converted from time or space domain to the frequency domai n usually through the Fourier transform. The Fourier transform converts the si gnal information to a magnitude and phase component of each frequency. Often the Fourier transform is converted to the power spectrum, which is the mag nitude of each frequency component squared.The most common purpose for analysis of signals in the frequency domai n is analysis of signal properties. The engineer can study the spectrum to dete rmine which frequencies are present in the input signal and which are missing .Filtering, particularly in non real-time work can also be achieved by conve rting to the frequency domain, applying the filter and then converting back to t he time domain. This is a fast, O (nlogn) operation, and can give essentially a ny filter shape including excellent approximations to brickwall filters.There are some commonly used frequency domain transformations. For example, the cepstrum converts a signal to the frequency domain Fourier tran sform, takes the logarithm, then applies another Fourier transform. This emph asizes the frequency components with smaller magnitude while retaining the o rder of magnitudes of frequency components.Frequency domain analysis is al so called spectrum or spectral analysis.5、signal processing,Signal usually need in different ways.For example, from a sensor output signal may be contaminated the redundant electrical "noise".Electrode is connected to a patient's chest, electrocardiogram (ecg) is measured by the heart and other muscles activity caused by small voltage variation.Due to the strong effect electrical interference from the power supply, signal picked up the"main" is usually adopted.Processing signal filter circuit can eliminate or at least reduce unwanted part of the signal.Now, more and more, is by the DSP technology to extract the signal filter to improve the quality of signal or important information, rather than the analog electronic technology.6、the development of DSPThe development of digital signal processing (DSP) in the 1960 s to large Numbers of digital computing applications using fast Fourier transform (FFT), which allows the frequency spectrum of a signal can be quicklycalculated.These techniques have not been widely used at the time, because suitable computing equipment is usually only in university and other research institutions can be used.7、the digital signal processor (DSP)In the late 1970 s and early 1980 s the introduction of microprocessor makes DSP technology is used in the wider range.General microprocessor, such as Intel x86 family, however, is not suitable for the calculation of DSP intensive demand, with the increase of DSP importance in the 1980 s led to several major electronics manufacturers (such as Texas instruments, analog devices and MOTOROLA) to develop a digital signal processor chip, microprocessor, specifically designed for use in the operation of the digital signal processing requirements type of architecture.(note that abbreviation DSP digital signal processing (DSP) of different meanings, this word is used in digital signal processing, a variety of technical or digital signal processor, aspecial type of microprocessor chips).As a common microprocessors, DSP is one kind has its own local instruction code of programmable devices.DSP chip is able to millions of floating point operations per second, as they are of the same type more famous universal device, faster and more powerful versions are introduced.DSP can also be embedded in a complex "system chip" devices, usually includes analog and digital circuit.8、the application of digital signal processorsDSP technology is widespread in mobile phones, multimedia computers, video recorders, CD players, hard disk drives and controller of the modem equipment, and will soon replace analog circuits in TV and telephone service.DSP is an important application of signal compression and decompression.Signal compression is used for digital cellular phone, in every place of the "unit" let more phone is processed at the same time.DSP signal compression technology not only makes people can talk to each other, and can be installed on the computer by using the small camera make people through the monitor to see each other, and these together is the only needs to be a traditional phone line.In audio CD system, DSP technology to perform complex error detection and correction of raw data, because it is read from CD.Although some of the underlying mathematical theory of DSP technology, such as Fourier transform and Hilbert transform, the design of digital filter and signal compression, can be quite complex, and the actual implementation of these technologies needed for numerical computation is very simple, mainlyincluding operations can be in a cheap four function calculator.A kind of structure design of the DSP chip to operate very fast, deal with the sample of the hundreds of millions of every second, and provide real-time performance: that is, to a real-time signal processing, because it is sample, and then the output signal processing, such as speakers or video display.All of the DSP applications mentioned above instance, such as hard disk drives and mobile phone, for real-time operation.Major electronics manufacturers have invested heavily in DSP technology.Because they now find application in mass-market products, DSP chip electronic device occupies very large proportion in the world market.Sales of billions of dollars a year, and may continue to grow rapidly.DSP is mainly used of audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communication, radar, sonar, earthquake, and biologicalmedicine.Concrete example is in digital mobile telephone voice compression and transmission, space balanced stereo matching, amplification area, good weather forecasts, economic forecasts, seismic data processing, and analysis of industrial process control, computer generated animation film, medical image such as CAT scans and magnetic resonance imaging (MRI),MP3compression, image processing, hi-fi speaker divider and equilibrium, and compared with electric guitar amplifier using audio effect.9、the experiment of digital signal processingDigital signal processing is often use special microprocessor, such as dsp56000 TMS320, or SHARC.These often processing data using the fixed point operation, although some versions can use floating-point arithmetic and more powerful.Faster application of FPGA can flow from a slow start the emergence of application processor Freescale company, traditional slower processors, such as single chip may be appropriate.数字信号处理1、介绍数字信号处理(DSP)的关心表示信号序列的数字或符号和处理这些信号。
Electrocardiogram signal processing method for exact Heart Rate detection in physical activity monitoring system: Wavelet Approach Uk Jin. Yoon, Yeon Sik. Noh, Young Myeon. Han,Min Yong. Kim, Jae Hoon. Jung, In Seop. Hwang,Hyung Ro. Yoon, member IEEEDept. of Biomedical EngineeringYonsei UniversityWonju, Republic of Koreayoonukjin@In Cheol. JeongIT Convergence Medical Instrument Research CenterNuga medical co., Ltd,Wonju, Republic of KoreaAbstract— Physical Activity Monitoring is a device that can measure the human activity quantity quantitatively through Heart Rate detection in real time. R-Spike detection of ECG is required for this Heart Rate detection. Since Physical Activity Monitoring System is usually used during activit y or exercise,however, signal measured in ECG System is contaminated by diverse noises.Diverse noises become the factors of failure in RS pike detection. Such factors impede the exact HR detection.This paper suggests method to con volute wavelet function and caling function as the optimum signal disposition method for optimum R-Spike detection. This method was compared with the R-Spike detection method that uses quadratic spline wavelet presented before. To verify performance of signal disposition method suggested in this paper, the ECG of noise stress test database (NSTDB) and MIT-Database were tested in combination. Then, the sensitivity of R-Spike detection rate for noise was also additionally tested by gradually lowering SNR of NSTDB. Then, it was verified through ECG signal that was actually measured in physical activity monitoringKeywords— skin temperature, heart rate variability, aerobic exerciseI. I NTRODUCTIONHeart Rate is one of the parameters of living body that can show the activity quantity of physical body gradually. As Heart Rate lineally increases with oxygen uptake ( 2 VO ) during exercise, it is used as important parameter of living body during exercise.[1]. Heart rate is measured by using Arterial blood pressure (ABP) and Photople thysmograph (PPG) or mostly Electrocardiogram (ECG). It is mainly measured by RSpike detection of ECG in the physical activity monitoring system. (RR interval, namely, the interval between R-Spike and next R-spike is calculated.) Thus, the failure of R-Spike detection makes exact HR detection difficult. R-Spike detection is usually hindered by noises such as Electrode Motion Artifact(EM), Muscle Artifact (MA) and Baseline wandering(BW). Diverse signal disposition methods to remove such diverse noises and detect R-Spike have been introduced in many papers.[2] Among them, wavelet method can locally express the form that includes the diverse patterns which can show signal in time-scale domain and occur in ECG cycle and includes the different frequencies (QRS Complex, T-wave, Pwave). Since noise and artifact that affect ECG can be divided into different frequency and different scale, it can be used as the most powerful ECG signal disposition tool[3]. As the typical wavelet method to dispose ECG signal, there is method to use quadratic spline wavelet. Since quadratic spline wavelethas the property of linear phase and first differential property, the position of R-Spike could be exactly detected by zero crossing method in dyadic scale [4]. However, quadratic spline wavelet showed abnormal detection in the strong noise that locally appeared. To improve the performance of such abnormal detection, this paper used the Daubechies’ orthonormal wavelet. Although orthonormal wavelet generally has linear phase property, it also has nonlinear phase property, thereby perverting ECG[5]. However, this point is not seriously considered in this study, because the objective of physical activity monitoring system is not medical diagnosis, but exact HR detection. The wavelet method suggested in this paper used Daubechies5 wavelet. The calculation is finished by 1 convolution between actual signal and newly formed wavelet function through convolution of Scaling function and Wavelet function.T he ECG describes the electrical activity of the heart. An ECG during a cardiac cycle consists of P, QRS and T waves. The detection of R-peaks and consequently were of the QRS complex in an abdominal electrocardiogram signal provides information on the heart rate and thus it is an important tool for the physician for identifying abnormalities in the heart activity. A complete overview of various approaches can be found in [1]. Various research efforts have been proposed in the literature including algorithms based on filtering and threshold methods, the most well known one being the Pan and Tompkins algorithm for peak detection but it requires an initial pre-determined peak detection threshold [2]. Most recent ones include wavelet methods [3]-[7], neural networks [8]-[10] and others, but all are threshold dependent. Recently, a threshold free method was introduced by M. Sheikh M. Algunaidi [11]. As claimed, Algunaidi's algorithm has facilitated the detection of the maternal peaks without pre-determined thresholds, using fixed length RR moving interval to detect the R peaks,calculated based on the normal maximum and minimum heart rate. This algorithm is able to detect the QRS peaks at different levels of threshold, without respecting threshold value. Since the moving interval requires enough samples for its second edge, some peaks are left undetected towards the end.This study introduces an alternative approach. Instead of using fixed length RR interval, we use varying length moving interval to obtain all the maternal QRS peaks present in the abdominal electrocardiogram (AECG) data so that undetected peaks are not left in the given number of samples.This paper is organized as follows. Section II describes the algorithm of the threshold free detection method [11], the proposed moving interval approach, and the maternal heart rate (MHR) evaluation process. Results are shown in Section III in terms of the sensitivity and positive predictivity and conclusion is drawn in Section IV.II. W AVELET M ETHODSA. Basic wavelet methodWavelet transform is basically composed of combination of basic function and signal by the process in formula ∫x(t) * a, b(t)dt(1).W (a, b) =As prototype wavelet, Formula (2) is expanded or reduced by using scale parameter ‘a’ and wavelet is conveyed by using movement parameter b that indicates the position in the time area. In this paper, wavelet is newly formed through convolution of scale function and wavelet function and the newly formed wavelet is obtained by formula (1).Multi-resolution Analysis that Mallet's suggested is the most well-known DWT algorithm it is composed of Filter bank with two sub-bands. Original signal is analyzed into two functions Scaling Function and Wavelet Function, by both of which the signal are each analyzed into Approximation and Detail Approximation is again analyzed into High-scale, Lowfrequency Component and the Details are analyzed into Lowscale, High-frequency.The process of returning Detail Coefficient is done as shown in Equation(4) x(t) is broken down by the expansion and movement of prototype function ψnamed Mother Wavelet. The signal from basis function projects the Detail Coefficient as shown in Equation(4). Where a b D , is known as the wavelet (or detail) coefficient at scale and location indices (a,b).Approximation Coefficient can be gained by the projection of the signal from expansion and movement of Scaling function.The scaling function can be convolved with the signal to produce approximation coefficients as shown in Equation(6).B. Suggest wavelet methodFirst, scale function and wavelet function were formed by using iteration in the time. Fourier transform m0(w) corresponding to wavelet filter h(k) can be indicated in the form of 2 - πcycle function as follows.Further, it is expressed as follows in the Fourier area of scale function and wavelet function.The above formula means that scale function can be defined by sequential calculation of filter coefficient. Namely, it is defined as follows as sequential convolution in the time.In Figure 1, the approximate value of scale function and wavelet function are acquired in k=1~5 by using Daubechie 5 filter coefficient. In the approximate value function, we newly formed function that can optimally detect R-Spike of scale function k=3 and wavelet function k=3 through convolution.Figure 1. Scale ring function and wavelet function are indicated from k=1 to k=5.Figure 2. Wavelet that is newly formed after convolution of formation process of scale ring function of k=3 and wavelet function with 2 functions.As the frequency range of R-Spike is in 10~40Hz, the -3db frequency response of wavelet function that is newly formed in Figure 2 should be in the position similar to that of frequency range of R-Spike. Testing the frequency response of actually designed wavelet, the result was between 11Hz and 45Hz. The wavelet of R-spike detection algorithm used in the paper was converted and negative peak was converted to positive peak through Squaring. Then, Smooth Filtering was done to increase the detection rate of QRS Complex through threshold. Next,R-Spike was detected through threshold methodIII. E XPERIMENTS AND RESULTWhile NSTDB provides 3 noise data, that is to say, Muscle Artifact(MA), Electrode Motion Artifact(EM) and baseline wander(BW), this experiment used EM and BW noise which mainly appear in the physical activity monitoring system. Since physical activity monitoring system mainly collects data from breast, it is hardly affected by ma noise. Colleting the actual data, ECG was hardly affected by ma noise. We combined and experimented EM and BW noise by gradually lowering SNR in recorder 100 of relatively clean MITdatabase.Test result is in Table 1. According to test, the performance of convolution wavelet (CNW) presented in the paper was better than that ofQuadratic spline wavelet (QSW). False Negative(FN) means that actual R-Spike is not detected and False Positive is detected even though it is not R-Spike. Then, sensitivity was calculated through FN and TP. TP is the number of total R-Spike detected by algorithm.Figure 3. Data made by lowering SNR of data formed by combining 100 Record of MIT-Data Base, EM noise of NSTDB and BW noise.False Negative(FN) means that actual R-Spike is not detected and False Positive is detected even though it is not R-Spike. Then, sensitivity was calculated through FN and TP. TP is the number of total R-Spike detected by algorithm.Figure 4. Data collected from portable hardware based on wearable sensor.As actual movement data, Figure 4 is data collected from portable hardware based on wearable sensor. Sampling. frequency is 500 Hz with 12 bit resolution. The data in the original Figure 4 is noise that locally appears in the actual exercise and the shape of such local noise is similar to that of R-Spike, thereby making R-Spike detection fail. If R-Spike detection fails, heart rate changes more widely, thereby causing false alarm on the system. The original data is the part that makes error when detecting with quadratic spline wavelet. However, detection could be done without error with the algorithm suggested in this paper.IV. CONCLUSIONThis study presented wavelet method that can optimize RSpike detection in physical activity monitoring system of noisy environment. 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