{"title":"Examination of temporal characteristic of sleep EEG subbands based on the local min-max","authors":"S. Janjarasjitt","doi":"10.1109/BMEICON.2012.6465463","DOIUrl":null,"url":null,"abstract":"In this paper, the temporal characteristic of spectral subbands of sleep EEG associated with different sleep stages is examined using a simple computational analysis technique based on the local minima and maxima. The average range of local min-max Rλ defined as the average differences between amplitudes of consecutive local minima and maxima is used as a temporal characteristic measure. From the computational results, it is observed that the sleep EEG epochs associated with different sleep stages manifest distinguishable characteristics of the average range of local min-max Rλ for various subbands. The computational results also show that the lower frequency subbands of sleep EEG associated with various sleep stages exhibit opposite temporal characteristic (the average range of local min-max Rλ) compared to the higher frequency subbands of sleep EEG. Furthermore, this suggests that the average range of local min-max Rλ may be a useful feature for sleep stage classification.","PeriodicalId":409705,"journal":{"name":"The 5th 2012 Biomedical Engineering International Conference","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 5th 2012 Biomedical Engineering International Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BMEICON.2012.6465463","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, the temporal characteristic of spectral subbands of sleep EEG associated with different sleep stages is examined using a simple computational analysis technique based on the local minima and maxima. The average range of local min-max Rλ defined as the average differences between amplitudes of consecutive local minima and maxima is used as a temporal characteristic measure. From the computational results, it is observed that the sleep EEG epochs associated with different sleep stages manifest distinguishable characteristics of the average range of local min-max Rλ for various subbands. The computational results also show that the lower frequency subbands of sleep EEG associated with various sleep stages exhibit opposite temporal characteristic (the average range of local min-max Rλ) compared to the higher frequency subbands of sleep EEG. Furthermore, this suggests that the average range of local min-max Rλ may be a useful feature for sleep stage classification.