I. S. Gendriz, D. Seoane, H. F. P. Quintero, A. S. Mengana
{"title":"Automatic detection of the end of the Empting Maneuvers in venous muscle pump test","authors":"I. S. Gendriz, D. Seoane, H. F. P. Quintero, A. S. Mengana","doi":"10.1109/STSIVA.2012.6340552","DOIUrl":null,"url":null,"abstract":"Venous refilling time (VRT) can diagnose the presence of venous system diseases. In order to calculate the VRT it is necessary to determine the End of the Empting Maneuvers (EEM). The automation of this process has been identified as a difficult task. Some methods described previously have some difficulties that could be solved. This paper proposes a new method for the automatic detection of the EEM. The new proposed method was compared with other based on sound signals. The annotations made by two trained human observers on 41 photoplethysmography records, were also used as a reference for evaluating the methods. The presented method was more accurate, showing a performance comparable to the observers.","PeriodicalId":383297,"journal":{"name":"2012 XVII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 XVII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STSIVA.2012.6340552","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Venous refilling time (VRT) can diagnose the presence of venous system diseases. In order to calculate the VRT it is necessary to determine the End of the Empting Maneuvers (EEM). The automation of this process has been identified as a difficult task. Some methods described previously have some difficulties that could be solved. This paper proposes a new method for the automatic detection of the EEM. The new proposed method was compared with other based on sound signals. The annotations made by two trained human observers on 41 photoplethysmography records, were also used as a reference for evaluating the methods. The presented method was more accurate, showing a performance comparable to the observers.