發(fā)布時間：2018-05-30 03:36

  本文選題：癲癇發(fā)作檢測 + 短時傅里葉變換　； 參考：《中國科學(xué)院研究生院(長春光學(xué)精密機械與物理研究所)》2014年碩士論文

【摘要】：癲癇(Epilepsy)是一種常見的腦功能障礙性疾病,其基本病理學(xué)表現(xiàn)為大量神經(jīng)元集群異常同步化放電,而頭皮腦電和皮層腦電是腦神經(jīng)電活動的反映,是研究癲癇發(fā)作的主要手段。基于腦電信號的癲癇發(fā)作自動檢測可以實現(xiàn)腦電分類和病灶定位,并能夠提高檢測效率,對癲癇的臨床治療具有重要意義。 針對累積能量特征不穩(wěn)定的問題,本文結(jié)合時頻分析方法和能量特征定義了時頻能量特征,并針對大數(shù)據(jù)量、高特征值空間的快速和準確分類問題,提出一種基于最大相關(guān)最小冗余準則和帶參數(shù)的極限學(xué)習(xí)機的癲癇發(fā)作自動檢測方法。首先,對皮層腦電和頭皮腦電分別使用短時傅里葉變換和希爾伯特變換,提取時頻分布的能量塊,并結(jié)合空間信息,得到時頻能量特征集。然后,利用序列前向選擇搜索策略生成不同大小的特征子集,其中的評價準則采用基于最大相關(guān)最小冗余的信息準則,以特征子集作為評價單位,使用基于分類準確率的特征選擇方法選擇最優(yōu)特征子集。最后,使用基于粒子群算法的支持向量機、基于粒子群算法的BP神經(jīng)網(wǎng)絡(luò)和帶參數(shù)的極限學(xué)習(xí)機對癲癇不同狀態(tài)進行分類和判別。 實驗結(jié)果： 1)在皮層腦電中,使用短時傅里葉變換提取的時頻能量特征的分類準確率為0.97,優(yōu)于使用經(jīng)驗?zāi)B(tài)分解提取的時頻能量特征。 2)使用基于粒子群算法的支持向量機、基于粒子群算法的BP神經(jīng)網(wǎng)絡(luò)和帶參數(shù)的極限學(xué)習(xí)機三種分類器進行10折交叉驗證,頭皮腦電的分類準確率為0.85左右,皮層腦電中包含發(fā)作期和亞臨床發(fā)作期的分類組的分類準確率為0.82左右,而發(fā)作間期和發(fā)作期的分類準確率達到0.97以上。 3)在皮層腦電的發(fā)作間期和發(fā)作期分類組,基于粒子群算法的支持向量機的分類準確率為0.98,訓(xùn)練時間為28.1s；基于粒子群算法的BP神經(jīng)網(wǎng)絡(luò)的分類準確率最高可達0.99,但是隨特征子集大小變化而有明顯的起伏,分類器性能不穩(wěn)定；帶參數(shù)的極限學(xué)習(xí)機的分類準確率為0.97,但訓(xùn)練時間僅為0.8s。帶參數(shù)的極限學(xué)習(xí)機的分類準確率和訓(xùn)練速度兩方面的綜合性能優(yōu)于基于粒子群算法的支持向量機和基于粒子群算法的BP神經(jīng)網(wǎng)絡(luò)。 4)利用帶參數(shù)的極限學(xué)習(xí)機對72小時的皮層腦電中數(shù)據(jù)進行連續(xù)檢測,誤檢率為1次/24小時,平均發(fā)作開始時刻檢測延遲為0.1s。 結(jié)果表明,在皮層腦電的發(fā)作期和發(fā)作間期分類中,基于短時傅里葉變換的時頻能量特征集是有效的�；谧畲笙嚓P(guān)最小冗余準則的序列前向選擇方法和帶參數(shù)的極限學(xué)習(xí)機的方法能夠?qū)崟r準確地檢測癲癇發(fā)作。
[Abstract]:Epilepsys is a common disorder of brain function. Its basic pathological manifestation is the abnormal synchronous discharge of a large number of neurons. The scalp EEG and cortical EEG are the reflection of EEG activity and the main means to study epileptic seizures. The automatic detection of epileptic seizures based on EEG signals can achieve EEG classification and focus location, and can improve the detection efficiency. It is of great significance for the clinical treatment of epilepsy. In this paper, the time-frequency energy features are defined by time-frequency analysis method and energy feature, and the fast and accurate classification problem of large data volume and high eigenvalue space is discussed. An automatic epileptic seizure detection method based on maximum correlation minimum redundancy criterion and parameter based extreme learning machine (LLM) is proposed. Firstly, the cortical EEG and scalp EEG are extracted from the energy blocks of time-frequency distribution by using short-time Fourier transform and Hilbert transform, respectively, and the time-frequency energy feature set is obtained by combining spatial information. Then, different size feature subsets are generated by the sequence forward selection search strategy. The evaluation criteria are based on the information criterion of maximum correlation and minimum redundancy, and the feature subset is used as the evaluation unit. A feature selection method based on classification accuracy is used to select the optimal feature subset. Finally, support vector machine based on particle swarm optimization, BP neural network based on particle swarm optimization and extreme learning machine with parameters are used to classify and distinguish different states of epilepsy. Experimental results: 1) in cortical EEG, the classification accuracy of time-frequency energy features extracted by short-time Fourier transform is 0.97, which is better than that extracted by empirical mode decomposition. 2) support vector machine based on particle swarm optimization, BP neural network based on particle swarm optimization and extreme learning machine with parameters are used for 10 fold cross validation. The accuracy of scalp EEG classification is about 0.85. The classification accuracy was about 0.82 in the cortical EEG group which included the attack period and subclinical attack stage, but the classification accuracy rate of the interictal phase and the attack stage was more than 0.97. 3) in the interictal and interictal groups of cortical EEG, The classification accuracy of support vector machine based on particle swarm optimization is 0.98 and the training time is 28.1 s. The classification accuracy of BP neural network based on particle swarm optimization is up to 0.99, but it fluctuates obviously with the change of feature subset size. The performance of the classifier is unstable, and the classification accuracy of the LLMs with parameters is 0.97, but the training time is only 0.8 s. The classification accuracy and training speed of LLMs with parameters are superior to those of SVM based on PSO and BP neural network based on PSO. 4) the data of 72 hours cortical electroencephalogram were continuously detected by the extreme learning machine with parameters. The false detection rate was 1 / 24 hours, and the detection delay was 0.1 s at the average onset time. The results show that the time-frequency energy feature set based on short-time Fourier transform is effective in the classification of cortical EEG seizures and interictal phases. The method of sequence forward selection based on maximum correlation minimum redundancy criterion and the method of extreme learning machine with parameters can detect seizures accurately and in real time.
【學(xué)位授予單位】：中國科學(xué)院研究生院(長春光學(xué)精密機械與物理研究所)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：R742.1;TP18

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本文編號：1953758