本文關(guān)鍵詞：基于大眾外包方法的紡錘波檢測可行性分析　出處：《西安電子科技大學》2015年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 睡眠紡錘波 在線睡眠研究問卷調(diào)查 在線檢測紡錘波系統(tǒng) 眾包

【摘要】：睡眠紡錘波(Spindle)是非快速眼動睡眠(NREM)N2階段的標志,它的頻率范圍為11~16Hz,產(chǎn)生時間大于0.5秒,振幅先增大后減小,形狀類似于梭子。紡錘波在人的記憶和智力的機制研究中,以及在一些精神類疾病的臨床診斷上都有重要作用。到目前為止,紡錘波自動檢測算法層出不窮,而基于肉眼觀測的手動劃分一直以來都是準確率最高的方法,同時專家的肉眼手動劃分被稱為紡錘波檢測的金標準,但是專家一般需要經(jīng)過專業(yè)培訓(xùn)而且很難找到,所以本文通過大眾外包的方法獲得了大量非專家標記的紡錘波數(shù)據(jù)集,然后得到非專家組的標準,并將其與專家組的金標準進行對比,來看看是否可以通過非專家組的標準來代替專家組的金標準。在進行本文的實驗之前,我們需要采集并選取一些實驗所用的睡眠腦電數(shù)據(jù)。在采集腦電數(shù)據(jù)之前,需要被試填寫一些量表來對被試近期的情緒、睡眠以及其他情況進行簡要評估,所以開發(fā)了在線睡眠研究問卷調(diào)查系統(tǒng)。該系統(tǒng)代替了傳統(tǒng)的紙質(zhì)問卷調(diào)查,一方面使得作用范圍更廣、速度更快,另一方面可以節(jié)省很大的人力、財力、物力和時間,而且可以將調(diào)查的數(shù)據(jù)全部存儲于計算機中,以便隨時使用。由于一個人一晚上的睡眠腦電數(shù)據(jù)量很大,而且本研究是通過大眾外包的方法來標記紡錘波的,所以參與標記紡錘波的被試會很多,因此最終需要處理的數(shù)據(jù)量會很大。如果采用MATLAB離線方式來標記紡錘波的話,后期數(shù)據(jù)統(tǒng)計和數(shù)據(jù)處理都會比較繁瑣,所以開發(fā)了基于WEB的在線檢測紡錘波系統(tǒng)。那么只需要被試身邊有一臺電腦就可以在線標記紡錘波,然后將標記的結(jié)果存儲到遠程數(shù)據(jù)庫。對專家組和非專家組通過在線檢測紡錘波系統(tǒng)標記的數(shù)據(jù)集進行分析,可以得到以下結(jié)論:對于專家組來說,組閾值T-group為0.3和重疊閾值T-overlap為0.45時,此時專家組金標準是最優(yōu)的。所有的專家與專家組金標準比較,其平均表現(xiàn)為0.84007±0.023(均值±方差),這表明每位專家與專家組金標準具有較好的一致性。對于非專家組來說,組閾值T-group為0.35和重疊閾值T-overlap為0.3時,非專家組標準是最優(yōu)的。所有的非專家與非專家組標準進行比較,其平均表現(xiàn)為0.7246±0.1008(均值±方差),顯然與專家組的平均表現(xiàn)相比,非專家組平均表現(xiàn)的均值變小,方差變大。這說明非專家之間的一致性不如專家的高。非專家組標準與專家組金標準比較的F1值為0.7557,也就是說雖然非專家之間的一致性沒有專家之間的高,但是非專家組標準與專家組金標準的差別程度還是可以接受的,即由非專家組標準代替專家組標準可行的。同時還將非專家組標準和RMS自動檢測算法進行對比,發(fā)現(xiàn)非專家組標準是優(yōu)于RMS自動算法的。
[Abstract]:Sleep spindle is a non-REM sleep NREMN _ 2 stage marker, its frequency range is 114Hzand the time of generation is more than 0.5 seconds. The amplitude increases first and then decreases, similar to the shape of the shuttle. Spindles play an important role in the study of the mechanism of human memory and intelligence, as well as in the clinical diagnosis of some mental disorders. So far. Automatic spindle wave detection algorithms emerge one after another, and manual partition based on naked eye observation has always been the most accurate method, and the expert manual partition is called the gold standard of spindle wave detection. However, experts usually need professional training and are difficult to find, so this paper obtains a large number of non-expert mark spindle wave data set through the method of public outsourcing, and then get the standard of non-expert group. And compare it with the gold standard of the expert group to see whether the gold standard of the expert group can be replaced by the standard of non-expert group. We need to collect and select sleep EEG data used in some experiments. Before we collect EEG data, we need to fill out a number of scales to briefly assess the participants' recent mood, sleep and other conditions. Therefore, an online sleep research questionnaire system has been developed. This system replaces the traditional paper questionnaire. On the one hand, it makes the function wider and faster, on the other hand, it can save a lot of manpower and financial resources. Material resources and time, and all the data can be stored in the computer, in order to use at any time. Because a person's sleep EEG data volume is very large. And this study is through the mass outsourcing method to mark the spindle wave, so many participants involved in marking spindle wave. Therefore, the amount of data to be processed will be very large. If the MATLAB off-line method is used to mark the spindle wave, the later data statistics and data processing will be more cumbersome. Therefore, an on-line spindle wave detection system based on WEB is developed, and only a computer is needed to mark the spindle wave online. The results of marking are then stored in the remote database. By analyzing the data sets of the expert group and the non-expert group through the on-line detection of the marking of the spindle wave system, the following conclusions can be drawn: for the expert group. When the group threshold T-group is 0.3 and the overlap threshold T-overlap is 0.45, the expert group gold standard is optimal. All experts are compared with the expert group gold standard. Its average performance is 0.84007 鹵0.023 (mean 鹵variance), which indicates that each expert has good consistency with expert group gold standard. When the group threshold T-group was 0.35 and the overlap threshold T-overlap was 0.3, the non-expert group criterion was the best. All the non-experts were compared with the non-expert group standard. Its average performance is 0.7246 鹵0.1008 (mean 鹵variance), obviously compared with the average performance of the expert group, the average performance of the non-expert group is smaller. The variance increases. This shows that the consistency among non-experts is not as high as that of experts. The F1 value of the non-expert group standard compared with the expert group gold standard is 0.7557. That is, although the consistency among non-experts is not as high as that among experts, the difference between the non-expert group criteria and the expert group gold criteria is acceptable. That is to say, it is feasible to replace the expert group standard with the non-expert group standard. At the same time, the comparison between the non-expert group standard and the RMS automatic detection algorithm shows that the non-expert group standard is superior to the RMS automatic algorithm.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：R740

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