{"title":"基于小波熵和禁忌搜索与粒子群优化相结合的听力损失识别","authors":"Chaosheng Tang, Elizabeth Lee","doi":"10.1109/ICDSP.2018.8631839","DOIUrl":null,"url":null,"abstract":"Sensorineural hearing loss is correlated to massive neurological or psychiatric disease. We treated a three-class classification problem: HC, LHL, and RHL, and checked three different orientation images: coronal, axial, and sagittal. Different methods are compared with 10x6-fold cross validation. The results show that our proposed system shows better performance in detecting hearing loss.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Hearing loss identification via wavelet entropy and combination of Tabu search and particle swarm optimization\",\"authors\":\"Chaosheng Tang, Elizabeth Lee\",\"doi\":\"10.1109/ICDSP.2018.8631839\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Sensorineural hearing loss is correlated to massive neurological or psychiatric disease. We treated a three-class classification problem: HC, LHL, and RHL, and checked three different orientation images: coronal, axial, and sagittal. Different methods are compared with 10x6-fold cross validation. The results show that our proposed system shows better performance in detecting hearing loss.\",\"PeriodicalId\":218806,\"journal\":{\"name\":\"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDSP.2018.8631839\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDSP.2018.8631839","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hearing loss identification via wavelet entropy and combination of Tabu search and particle swarm optimization
Sensorineural hearing loss is correlated to massive neurological or psychiatric disease. We treated a three-class classification problem: HC, LHL, and RHL, and checked three different orientation images: coronal, axial, and sagittal. Different methods are compared with 10x6-fold cross validation. The results show that our proposed system shows better performance in detecting hearing loss.
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