通过肺活量测量仪器的呼吸流量传感:使用遗传算法的病理预测

A. Lay-Ekuakille, G. Vendramin, A. Trotta
{"title":"通过肺活量测量仪器的呼吸流量传感:使用遗传算法的病理预测","authors":"A. Lay-Ekuakille, G. Vendramin, A. Trotta","doi":"10.1109/ICSENST.2008.4757120","DOIUrl":null,"url":null,"abstract":"Spirometry takes care to find and to predict respiratory system pathologies through instrumentation that mainly carries out measurements on the volume and the air flow expired from lungs. A complete spirometric instrumentation composed of three parts has been developed. The first part, ldquohardwarerdquo, gains a sampled signal from a sensor of the flow-time curve and sends it to the computer. The second part, ldquosoftwarerdquo, processes received data calculating the volume-time curve, the flow-volume curve and other main spirometric parameters, displaying the result of prediction. The last part, ldquoa genetic algorithmrdquo, trains itself on the base of a series of computing with real data, to produce spirometric parameters of a most likely pathologic curve and, to predict pathology type with less possible tests.","PeriodicalId":6299,"journal":{"name":"2008 3rd International Conference on Sensing Technology","volume":"49 1","pages":"313-317"},"PeriodicalIF":0.0000,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Breath flow sensing via spirometric instrumentation: Pathology prediction using a genetic algorithm\",\"authors\":\"A. Lay-Ekuakille, G. Vendramin, A. Trotta\",\"doi\":\"10.1109/ICSENST.2008.4757120\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Spirometry takes care to find and to predict respiratory system pathologies through instrumentation that mainly carries out measurements on the volume and the air flow expired from lungs. A complete spirometric instrumentation composed of three parts has been developed. The first part, ldquohardwarerdquo, gains a sampled signal from a sensor of the flow-time curve and sends it to the computer. The second part, ldquosoftwarerdquo, processes received data calculating the volume-time curve, the flow-volume curve and other main spirometric parameters, displaying the result of prediction. The last part, ldquoa genetic algorithmrdquo, trains itself on the base of a series of computing with real data, to produce spirometric parameters of a most likely pathologic curve and, to predict pathology type with less possible tests.\",\"PeriodicalId\":6299,\"journal\":{\"name\":\"2008 3rd International Conference on Sensing Technology\",\"volume\":\"49 1\",\"pages\":\"313-317\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 3rd International Conference on Sensing Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSENST.2008.4757120\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 3rd International Conference on Sensing Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSENST.2008.4757120","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

肺活量测定法主要通过测量肺活量和肺活量的仪器来发现和预测呼吸系统的病变。研制了一套由三部分组成的完整肺活量测量仪器。第一部分是硬件部分,从流量-时间曲线传感器获取采样信号并将其发送到计算机。第二部分ldquosoftwarerdquo对接收到的数据进行处理,计算容积-时间曲线、流量-体积曲线等主要肺功能参数,并显示预测结果。最后一部分,ldquoa遗传算法,在一系列实际数据计算的基础上进行自我训练,产生最可能的病理曲线的肺活量参数,并通过较少可能的测试来预测病理类型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Breath flow sensing via spirometric instrumentation: Pathology prediction using a genetic algorithm
Spirometry takes care to find and to predict respiratory system pathologies through instrumentation that mainly carries out measurements on the volume and the air flow expired from lungs. A complete spirometric instrumentation composed of three parts has been developed. The first part, ldquohardwarerdquo, gains a sampled signal from a sensor of the flow-time curve and sends it to the computer. The second part, ldquosoftwarerdquo, processes received data calculating the volume-time curve, the flow-volume curve and other main spirometric parameters, displaying the result of prediction. The last part, ldquoa genetic algorithmrdquo, trains itself on the base of a series of computing with real data, to produce spirometric parameters of a most likely pathologic curve and, to predict pathology type with less possible tests.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Synthesis and characterization of CuO nanoparticle: Its electrochemical paracetamol sensor activity and substituted-2-aminothiophene synthesis applications Electrochemical-sensor, antimicrobial and Environmental Assessments of Bi 3+ :Mg (1− x) Zr x O 4 NPs synthesized by bio-mediated combustion method CeO2/ZnO nanocomposite-modified glassy carbon electrode as an enhanced sensing platform for sensitive voltammetric determination of norepinephrine Mechanical and corrosion properties of electrochemically deposited Ni-Nb2O5 composite coatings on mild steel for marine Applications Voltammetric analysis of hazardous azo-dye indigo carmine at simple and cost-effective carbon paste sensor modified with L-phenylalanine
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1