E. Uzunhisarcikli, T. Koza, M. Kaya, İdris Ardiç
{"title":"二尖瓣多普勒信号特征的测定","authors":"E. Uzunhisarcikli, T. Koza, M. Kaya, İdris Ardiç","doi":"10.1109/BIYOMUT.2010.5479816","DOIUrl":null,"url":null,"abstract":"In this study, entropy is calculated from sub-bands of clinical mitral valve Doppler signals DWT (Discrete Wavelet Tranform). Calculated entropy values are compared for healthy and patient mitral valve regurgitation. This study comprises two stages. In the first stage, mitral valve Doppler signals are divided sub-bands with DWT and changed values are determined on the frequency spectrum. In the second stage, entropy are calculated each sub-bands which determined with DWT. As a result, different features are obtained between mitral valve regurgitation and healthy patients. These features can help to diagnosis using artificial intelligence techniques or expert systems.","PeriodicalId":180275,"journal":{"name":"2010 15th National Biomedical Engineering Meeting","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Determination of feature for mitral valve Doppler signals\",\"authors\":\"E. Uzunhisarcikli, T. Koza, M. Kaya, İdris Ardiç\",\"doi\":\"10.1109/BIYOMUT.2010.5479816\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, entropy is calculated from sub-bands of clinical mitral valve Doppler signals DWT (Discrete Wavelet Tranform). Calculated entropy values are compared for healthy and patient mitral valve regurgitation. This study comprises two stages. In the first stage, mitral valve Doppler signals are divided sub-bands with DWT and changed values are determined on the frequency spectrum. In the second stage, entropy are calculated each sub-bands which determined with DWT. As a result, different features are obtained between mitral valve regurgitation and healthy patients. These features can help to diagnosis using artificial intelligence techniques or expert systems.\",\"PeriodicalId\":180275,\"journal\":{\"name\":\"2010 15th National Biomedical Engineering Meeting\",\"volume\":\"65 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 15th National Biomedical Engineering Meeting\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIYOMUT.2010.5479816\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 15th National Biomedical Engineering Meeting","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIYOMUT.2010.5479816","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Determination of feature for mitral valve Doppler signals
In this study, entropy is calculated from sub-bands of clinical mitral valve Doppler signals DWT (Discrete Wavelet Tranform). Calculated entropy values are compared for healthy and patient mitral valve regurgitation. This study comprises two stages. In the first stage, mitral valve Doppler signals are divided sub-bands with DWT and changed values are determined on the frequency spectrum. In the second stage, entropy are calculated each sub-bands which determined with DWT. As a result, different features are obtained between mitral valve regurgitation and healthy patients. These features can help to diagnosis using artificial intelligence techniques or expert systems.