本文選題：腦電圖 + 分類　； 參考：《蘭州大學(xué)》2017年碩士論文

【摘要】：最近以來,隨著深度學(xué)習(xí)和人工智能技術(shù)的快速進(jìn)步,研究人員開始借助于這些新技術(shù)來研究關(guān)于腦電圖的問題。使用腦電圖,醫(yī)生能夠更好地診斷腦部疾病;研究人員也能夠更好地了解腦電波與行為活動(dòng)之間的關(guān)系,從而研發(fā)更加智能的設(shè)備。本文通過將腦電圖測量儀器采集的腦電圖數(shù)據(jù)作為輸入,將對應(yīng)的人的眼睛的狀態(tài)作為輸出來進(jìn)行腦電圖的研究。為有效提高腦電圖數(shù)據(jù)眼睛狀態(tài)分類的可靠性及精確度,本文根據(jù)腦電圖數(shù)據(jù)的規(guī)律及腦電圖在人睜眼閉眼時(shí)數(shù)據(jù)變化的特征,提出了基于數(shù)據(jù)異常值處理的隨機(jī)森林和kNN模型。本文首先對原始數(shù)據(jù)進(jìn)行數(shù)據(jù)預(yù)處理:這部分主要包含對數(shù)據(jù)的缺失值處理、異常值處理和一致性分析;對于本文使用的數(shù)據(jù),我們使用統(tǒng)計(jì)量分析和分維可視化圖來處理數(shù)據(jù)集中的異常值。然后,在數(shù)據(jù)進(jìn)行異常值處理后,使用隨機(jī)森林和kNN建立具體的模型。對于隨機(jī)森林,主要對模型的OOB誤差率和變量的重要性進(jìn)行討論;對于kNN模型,由于k值對模型比較關(guān)鍵,本文通過在訓(xùn)練集上采用交叉驗(yàn)證的方法來確定k值,進(jìn)而使用確定后的k值來進(jìn)行測試集的評價(jià)。最后為了顯示隨機(jī)森林和kNN算法這兩個(gè)模型在該數(shù)據(jù)集上的有效性,本文使用決策樹、Bagging和SVM模型作為對比方法,進(jìn)行模型的比較,同時(shí)也討論了數(shù)據(jù)集中樣本的不均衡性對模型的影響。結(jié)果表明:本文提出的基于異常值處理的隨機(jī)森林和kNN模型具有更好的預(yù)測準(zhǔn)確度,隨機(jī)森林的預(yù)測精確度達(dá)到92.9392%,kNN算法的預(yù)測精確度達(dá)到97.0946%。由此,隨機(jī)森林和kNN算法都是有效的該腦電圖數(shù)據(jù)的預(yù)測模型,尤其是kNN算法,相比本文中的其他方法,其具有最好的預(yù)測效果。
[Abstract]:Recently, with the rapid progress of deep learning and artificial intelligence, researchers have begun to use these new technologies to study the problems of electroencephalograph. Using electroencephalograph, doctors can better diagnose brain diseases; researchers can also better understand the relationship between brain waves and behavioral activities, so as to develop more intelligence. In order to effectively improve the reliability and accuracy of the classification of the eye state of the electroencephalogram data, this article is based on the law of the EEG data and the eyes closed to the eyes. In this paper, a random forest and kNN model based on data abnormity processing is proposed. Firstly, the original data is preprocessed: this part mainly includes missing value processing, outlier processing and consistency analysis. For the data used in this paper, we use statistics and fractal visualization To deal with the abnormal values of the data set. Then, after the data is processed, a specific model is established using the random forest and kNN. For the random forest, the importance of the OOB error rate and the importance of the variable is discussed. For the kNN model, because the K value is more critical to the model, this paper uses cross validation on the training set. In order to show the validity of the two models of the random forest and the kNN algorithm, the decision tree, the Bagging and the SVM model are used as comparison methods to make the comparison between the two models of the random forest and the kNN algorithm, and the disequilibrium of the data concentration samples is also discussed. The results show that the stochastic forest and kNN model proposed in this paper have better prediction accuracy, the prediction accuracy of the random forest is 92.9392%, the prediction accuracy of the kNN algorithm is 97.0946%., and the random forest and the kNN algorithm are all the effective prediction models of the EEG data. It is the kNN algorithm. Compared with other methods in this paper, it has the best prediction effect.
【學(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R318;TP18

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本文編號：2098196