M. Yoneda, Hiroshi Tasaki, N. Tsuchiya, H. Nakajima, Takehiro Hamaguchi, Shojiro Oku, T. Shiga
{"title":"实用内脏脂肪估计的生物电阻抗分析方法研究","authors":"M. Yoneda, Hiroshi Tasaki, N. Tsuchiya, H. Nakajima, Takehiro Hamaguchi, Shojiro Oku, T. Shiga","doi":"10.1109/GrC.2007.109","DOIUrl":null,"url":null,"abstract":"The method of bioelectrical impedance-based visceral fat estimation is in advance of other methods such as X-ray CT or MRI from the point views of cost and safety. However, it requires complex and diversity signal analysis and modeling to realize its high estimation accuracy. In response to this requirement, complication of feature attributes and simplification in selection of them to model the estimation has been proposed and evaluated in this paper. The complication of feature attributes is realized by applying priori knowledge and by employing the idea of cardinality as quantitative evaluation index. The simplification of the estimation model is realized by employing Akaike information criteria. The experiments were conducted to evaluate the proposed method and the results prove high estimation accuracy and stability of the proposed method.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":"{\"title\":\"A Study of Bioelectrical Impedance Analysis Methods for Practical Visceral Fat Estimation\",\"authors\":\"M. Yoneda, Hiroshi Tasaki, N. Tsuchiya, H. Nakajima, Takehiro Hamaguchi, Shojiro Oku, T. Shiga\",\"doi\":\"10.1109/GrC.2007.109\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The method of bioelectrical impedance-based visceral fat estimation is in advance of other methods such as X-ray CT or MRI from the point views of cost and safety. However, it requires complex and diversity signal analysis and modeling to realize its high estimation accuracy. In response to this requirement, complication of feature attributes and simplification in selection of them to model the estimation has been proposed and evaluated in this paper. The complication of feature attributes is realized by applying priori knowledge and by employing the idea of cardinality as quantitative evaluation index. The simplification of the estimation model is realized by employing Akaike information criteria. The experiments were conducted to evaluate the proposed method and the results prove high estimation accuracy and stability of the proposed method.\",\"PeriodicalId\":259430,\"journal\":{\"name\":\"2007 IEEE International Conference on Granular Computing (GRC 2007)\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"30\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE International Conference on Granular Computing (GRC 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GrC.2007.109\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Conference on Granular Computing (GRC 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GrC.2007.109","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Study of Bioelectrical Impedance Analysis Methods for Practical Visceral Fat Estimation
The method of bioelectrical impedance-based visceral fat estimation is in advance of other methods such as X-ray CT or MRI from the point views of cost and safety. However, it requires complex and diversity signal analysis and modeling to realize its high estimation accuracy. In response to this requirement, complication of feature attributes and simplification in selection of them to model the estimation has been proposed and evaluated in this paper. The complication of feature attributes is realized by applying priori knowledge and by employing the idea of cardinality as quantitative evaluation index. The simplification of the estimation model is realized by employing Akaike information criteria. The experiments were conducted to evaluate the proposed method and the results prove high estimation accuracy and stability of the proposed method.