【摘要】：在臨床研究中，以刪失數(shù)據(jù)更為常見。由于死亡時間的不確定，只能將其看做是一個大的范圍，又由于點過程理論的前提是假設(shè)點事件發(fā)生在一個很小的范圍之內(nèi)，因而刪失數(shù)據(jù)不能將其看做為一個點事件。對刪失數(shù)據(jù)的點過程化和生存數(shù)據(jù)建模與分析是一個重要的課題。本文提出了基于點過程理論的生存函數(shù)非參數(shù)估計，并將該方法應(yīng)用到模擬數(shù)據(jù)和真實乳腺癌患者數(shù)據(jù)的生存曲線的非參數(shù)估計，并同傳統(tǒng)SC方法進行比較。 熵廣泛的應(yīng)用到兩組腦電信號和心電信號檢測與分類中，并得到了較好的效果。但熵只提取了混沌信號中的復(fù)雜程度，忽略了兩種信號中最大幅值的區(qū)別，因此在兩個復(fù)雜程度相似但幅值有很大差別的信號中，，熵的分類效果就大大降低。將信號中最大幅值信息添加到樣本熵的方法中，本文提出一種基于多元點過程熵的信號分類方法。將該方法應(yīng)用到波恩大學(xué)癲癇病中心公開腦電數(shù)據(jù)，其分類結(jié)果同只基于原始腦電數(shù)據(jù)的傳統(tǒng)多元多尺度熵的分類結(jié)果進行對比，結(jié)果比較表明本文提出的方法的精確度高于傳統(tǒng)多元多尺度熵。最后向波恩癲癇腦電數(shù)據(jù)加入不同等級的白噪聲，并同時應(yīng)用本文方法和傳統(tǒng)多元多尺度熵進行分類，結(jié)果比較說明：多元點過程熵在對干擾性大的信號分析中較傳統(tǒng)方法有更高的精度。
[Abstract]:Deletion of data is more common in clinical studies. Because of the uncertainty of the time of death, it can only be regarded as a large range, and because the premise of the point process theory is that the point event is assumed to occur in a very small range, the censored data cannot be regarded as a point event. It is an important task to model and analyze the point process and survival data of censored data. In this paper, the nonparametric estimation of survival function based on point process theory is proposed. The proposed method is applied to the nonparametric estimation of survival curve between simulated data and real breast cancer data, and compared with the traditional SC method. Entropy is widely used in the detection and classification of two groups of EEG and ECG signals. However, entropy can only extract the complexity of chaotic signals and ignore the difference of the largest values in the two signals. Therefore, in two signals with similar complexity but great difference in amplitude, the classification effect of entropy is greatly reduced. In this paper, a signal classification method based on multivariate point process entropy is proposed by adding the maximum value information to the sample entropy. The method was applied to the published EEG data from the epilepsy center of the University of Bonn. The classification results were compared with the traditional multivariate multi-scale entropy classification results based only on the original EEG data. The results show that the accuracy of the proposed method is higher than that of the traditional multivariate multi-scale entropy. Finally, different levels of white noise are added to the epileptic EEG data in Bonn, and at the same time, the method of this paper and the traditional multi-scale entropy are used to classify the EEG data. The results show that the multivariate point process entropy has higher accuracy than the traditional method in signal analysis with large interference.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.6;R742.1

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