摘要

研究神經(jīng)系統(tǒng)群體特征，既需要得到多個(gè)神經(jīng)元同一時(shí)間的信息，又需要掌握單個(gè)神經(jīng)元的放電序列。多電極細(xì)胞外記錄是對(duì)神經(jīng)系統(tǒng)進(jìn)行研究的基本手段。多電極細(xì)胞外記錄的方法經(jīng)過(guò)了一定時(shí)間的發(fā)展，已經(jīng)得到了廣泛的應(yīng)用。但是對(duì)多電極記錄信號(hào)的處理一直是個(gè)難題。研究神經(jīng)系統(tǒng)群體特征，需要掌握單個(gè)神經(jīng)元的放電序列，然而細(xì)胞外記錄到的信號(hào)一方面夾雜大量背景噪音，另一方面是電極區(qū)域多個(gè)神經(jīng)元放電動(dòng)作的疊加。如何將神經(jīng)電信號(hào)從原始信號(hào)中準(zhǔn)確提取出來(lái)，得知信號(hào)記錄到的是多少個(gè)神經(jīng)元活動(dòng)的疊加，并且將信號(hào)中的動(dòng)作電位歸類于單個(gè)神經(jīng)元，是一切研究解碼過(guò)程的基礎(chǔ)。目前已經(jīng)有很多種方法，但是第一步動(dòng)作電位檢測(cè)的方法始終不盡如人意。而如果這一步的結(jié)果不準(zhǔn)確，后面的工作都仿佛空中樓閣。因此本文試圖找到一種比較好的動(dòng)作電位檢測(cè)方法。
本文提出的方法是，首先利用常用的閾值檢測(cè)方法對(duì)原始數(shù)據(jù)進(jìn)行初步的動(dòng)作電位檢測(cè)，然后利用主成分分析方法以及減法聚類獲得動(dòng)作電位的平均模板，以動(dòng)作電位的平均波形作為形態(tài)學(xué)濾波器，重新對(duì)原始數(shù)據(jù)進(jìn)行動(dòng)作電位檢測(cè)。此方法使用在模擬數(shù)據(jù)中，在各種噪聲強(qiáng)度下效果均比閾值法有所提高，并且在存在基線漂移的情況下效果明顯較好。最后將算法用到采集的實(shí)驗(yàn)數(shù)據(jù)樣本中。

關(guān)鍵詞：動(dòng)作電位檢測(cè)，動(dòng)作電位分類，閾值檢測(cè)，形態(tài)學(xué)濾波器









DETECTION AND SORTING OF ACTION POTENTIALS RECORDED BY MULTI-ELECTRODE SYSTEM

ABSTRACT

To understand the population behaviors in nervous system, we need both the real-time information each neuron carries, and the exact firing sequence of the individual neuron. Multi-electrode system is the fundamental tool for research in nervous system. With its development after years, multi-electrode system has been widely used. But the process of the signal extracted from multi-electrode system is still a big problem. We want the exact firing sequence of individual neuron, however, the signal is corrupted with a large amount of background noise and the signal may involve the firing activities of more than one neuron. To get all the spikes, count the number of neurons contributing to the signal, and find the neuron that fires each spike is the very first step of all the research. Many methods have been developed, but the first step, spike detection is still not satisfying. Therefore, in this paper, a better method of spike detection is expected.
This paper proposes a method combining threshold detection and morphological filter. Firstly, apply the threshold detection to the recorded data, and cluster the spikes with principal component analysis. Secondly, take the template of one cluster of spikes as the morphological filter. Finally, filter the raw data with the best morphological filter and redetect the filtered signal. This method works better than threshold detection with varied signal noise ratio. When the baseline shifts, the new method is not influenced while the threshold detection is apparently inferior. The method is also used to process real data.

Key words: spike detection, spike sorting, threshold detection, morphological filter



目錄
第一章 緒論…………………………………………………………………………………..1
1.1神經(jīng)元?jiǎng)幼麟娢挥涗浵到y(tǒng) 1
1.2神經(jīng)元?jiǎng)幼麟娢粰z測(cè)的意義 2
1.3動(dòng)作電位檢測(cè)的方法及發(fā)展概述 3
1.3.1閾值檢測(cè)法 3
1.3.2窗口檢測(cè) 4
1.3.3基于非線性能量算子的檢測(cè) 4
1.3.4 匹配濾波方法 5
1.3.5 基于概率的檢測(cè) 5
1.3.6基于小波變換的檢測(cè) 5
1.4神經(jīng)元?jiǎng)幼麟娢坏姆诸惙椒?6
1.4.1模板匹配 6
1.4.2基于特征分析的分類方法 6
1.4.3 聚類方法 7
1.5本章小結(jié) 8
第二章 材料與方法 10
2.1 模擬數(shù)據(jù)方法 10
2.2神經(jīng)元?jiǎng)幼麟娢恍盘?hào)多電極記錄系統(tǒng) 11
2.2.1 多電極陣列 11
2.2.2視網(wǎng)膜標(biāo)本 12
2.2.3灌流系統(tǒng) 12
2.2.4 刺激和記錄系統(tǒng) 12
2.3 基于形態(tài)學(xué)濾波器的方法 13
2.3.1動(dòng)作電位檢測(cè)方法 14
2.3.2動(dòng)作電位分類方法 15
2.4 本章小結(jié) 16
第三章 結(jié)果 17
3.1將所提出算法用于模擬數(shù)據(jù) 17
3.1.1閾值法檢測(cè)與主成分分析 17
3.1.2形態(tài)學(xué)濾波器的構(gòu)建與濾波 20
3.2兩種方法對(duì)比結(jié)果 22
3.3模擬基線漂移數(shù)據(jù)結(jié)果 23
3.4真實(shí)實(shí)驗(yàn)數(shù)據(jù)結(jié)果 25
3.5本章小結(jié) 28
第四章 總結(jié)與展望 30
謝辭………………………………..…………………………………………………………33
原文及譯文 34