{"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}
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.