【摘要】：癲癇是一種嚴重的中樞神經系統紊亂疾病,以發(fā)作的反復性和不可預知性為特征。腦電(EEG)信號是監(jiān)測和診斷癲癇發(fā)作的一個重要途徑。然而,傳統EEG監(jiān)測只能在醫(yī)院完成,不利于癲癇患者的日常監(jiān)測,又由于EEG信號的復雜性,給人工診斷癲癇發(fā)作帶來很大挑戰(zhàn)。本文提出了一套綜合性的移動醫(yī)療監(jiān)測系統,對EEG信號進行便攜式采集、傳輸,并根據EEG信號的信息對癲癇發(fā)作進行自動分析和檢測。系統包括兩個主要模塊:前端模塊和后端模塊。前端模塊是一套基于ADS1299芯片的便攜式EEG信號采集前端,包括四個模塊:信號采集、控制、數據存儲和數據傳輸。摒棄了以往腦電采集設備所必備的模擬和數字兩大電路部分的設計方式,而是采用了目前比較先進的ADS1299芯片對EEG進行放大、濾波、采樣等其他多種操作。該套采集設備沒有采用高頻高性能的控制芯片,而是采用將數據處理程序放到上位機的方式,這樣只需要采用更低處理頻率的MCU,既保持著應有的功能,降低了整套系統價格,也全面發(fā)揮著后端模塊的優(yōu)勢。與后端模塊的連接方面,考慮到設備的便攜式特點,該設備摒棄了傳統的USB有線連接方式,采用WIFI方式可以方便的與多種類的上位機進行自由的連接,擴展了有效連接距離。在電源方面,該設備采用目前流行的移動電源,有效消除50Hz工頻干擾,進一步增強設備的穩(wěn)定性。后端模塊包括預處理、特征提取和分類。本文以公共數據庫中相關癲癇EEG數據為例,首先對原始EEG信號進行帶通和陷波濾波,去除偽跡和工頻干擾;然后進行小波分解,進而根據小波能量熵進行特征提取,最后采用支持向量機將信號分為未發(fā)作和發(fā)作兩種狀態(tài),完成癲癇發(fā)作的探測。仿真結果證明了所提出方案的有效性。
[Abstract]:Epilepsy is a severe disorder of the central nervous system characterized by recurrent and unpredictable seizures. EEG (EEG) signal is an important way to monitor and diagnose epileptic seizures. However, traditional EEG monitoring can only be done in hospitals, which is not conducive to the routine monitoring of epilepsy patients, and because of the complexity of EEG signals, it brings a great challenge to the artificial diagnosis of epileptic seizures. This paper presents a comprehensive mobile medical monitoring system, which can collect and transmit EEG signals in a portable manner, and analyze and detect epileptic seizures automatically according to the information of EEG signals. The system includes two main modules: front-end module and back-end module. The front-end module is a portable EEG signal acquisition front-end based on ADS1299 chip, which includes four modules: signal acquisition, control, data storage and data transmission. The design method of analog and digital circuits necessary for EEG acquisition equipment is abandoned, and the more advanced ADS1299 chip is used to amplify, filter and sample EEG. Instead of using the high frequency and high performance control chip, the acquisition equipment adopts the method of putting the data processing program into the upper computer, which only needs MCU with lower processing frequency, which not only keeps the proper function, but also reduces the price of the whole system. Also give full play to the advantages of the back-end module. In connection with the back-end module, considering the portable characteristic of the device, the device abandons the traditional USB wired connection mode, and adopts the WIFI mode to connect freely with many kinds of host computer conveniently, and extends the effective connection distance. In the aspect of power supply, the equipment adopts the current popular mobile power supply, effectively eliminates the 50Hz power frequency interference, and further enhances the stability of the equipment. The back-end module includes preprocessing, feature extraction and classification. In this paper, we take the relevant epileptic EEG data in the common database as an example. First, the original EEG signal is filtered by bandpass and notch wave to remove artifact and power frequency interference, then wavelet decomposition is carried out, and then the feature extraction is carried out according to wavelet energy entropy. Finally, support vector machine (SVM) is used to detect epileptic seizures. Simulation results show the effectiveness of the proposed scheme.
【學位授予單位】：天津職業(yè)技術師范大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：R742.1

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