癲癇腦電移動監(jiān)測系統(tǒng)設(shè)計
發(fā)布時間：2018-08-14 19:31
【摘要】：癲癇是一種嚴(yán)重的中樞神經(jīng)系統(tǒng)紊亂疾病,以發(fā)作的反復(fù)性和不可預(yù)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(tǒng)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(fù)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(tǒng),對EEG信號進(jìn)行便攜式采集、傳輸,并根據(jù)EEG信號的信息對癲癇發(fā)作進(jìn)行自動分析和檢測。系統(tǒng)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(shù)據(jù)存儲和數(shù)據(jù)傳輸。摒棄了以往腦電采集設(shè)備所必備的模擬和數(shù)字兩大電路部分的設(shè)計方式,而是采用了目前比較先進(jìn)的ADS1299芯片對EEG進(jìn)行放大、濾波、采樣等其他多種操作。該套采集設(shè)備沒有采用高頻高性能的控制芯片,而是采用將數(shù)據(jù)處理程序放到上位機(jī)的方式,這樣只需要采用更低處理頻率的MCU,既保持著應(yīng)有的功能,降低了整套系統(tǒng)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(shè)備的便攜式特點,該設(shè)備摒棄了傳統(tǒng)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(jī)進(jìn)行自由的連接,擴(kuò)展了有效連接距離。在電源方面,該設(shè)備采用目前流行的移動電源,有效消除50Hz工頻干擾,進(jìn)一步增強(qiáng)設(shè)備的穩(wěn)定性。后端模塊包括預(yù)處理、特征提取和分類。本文以公共數(shù)據(jù)庫中相關(guān)癲癇EEG數(shù)據(jù)為例,首先對原始EEG信號進(jìn)行帶通和陷波濾波,去除偽跡和工頻干擾;然后進(jìn)行小波分解,進(jìn)而根據(jù)小波能量熵進(jìn)行特征提取,最后采用支持向量機(jī)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(jié)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(xué)位授予單位】：天津職業(yè)技術(shù)師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：R742.1
本文編號：2183903
[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(xué)位授予單位】：天津職業(yè)技術(shù)師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：R742.1
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