### 博碩士論文 972201028 詳細資訊

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(Automatic Detection and Classification of Bursts in Brain Thalamus Neurons)

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First, we will introduce how nerve cells generate action potentials and the background information of nerve signals. Next, according to the bursts selected by a biologist through experience, we will generalize the characteristics and establish three screening conditions. These three screening conditions are as follows: associated with time, the gap condition, associated with amplitude, the decay condition, and associated with waveforms, the shape condition. With these conditions, we will then apply them on to a set of raw data to detect bursts. Before detecting the signals, we will first process the raw data. This includes down sampling and filtering. After processing the raw data, we will automatically detect the bursts using the three screening conditions mentioned above. In addition, using the Principal Component Analysis (PCA), we will then classify the bursts.
Testing with the bursts that the biologist has selected based on his experience, the results collected through the filtering is confirmed. Moreover, the bursts detected from the raw data using the filtering criteria also pass the test. Therefore, we can see that the filtering criteria can reflect the characteristics of bursts and effectively detect them.

★ 連發波

★ nerve impulse

2 背景知識簡介
3 實際訊號處理與分析
3.1 訊號的前置處理
3.2 分析訊號與建立篩選條件
3.2.1 間隔條件
3.2.2 遞減條件
3.2.3 形似條件
3.3 篩選條件的測試
3.4 處理原始訊號以及 bursts 的分類
4 振幅衰減的統計模型
4.1 數值模擬一：Y = RX
4.2 數值模擬二：μY = rμX
4.3 數值模擬三：Y = rX + N
4.4 數據模擬結果與真實數據之比較
5 結論與展望
5.1 結論與探討
5.2 檢討與建議

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