癲癇為一常見的神經系統疾病，根據我國2000至2003年的健保資料顯示，台灣地區每千人的癲癇患病數為5.58人，每十萬人的發病數則有97人。然而，當中有近30%的患者無法藉由藥物控制癲癇的發作，面對這種情況，需以手術的方式進行治療。近來研究顯示，由顱內腦電波圖量測的高頻震盪(HFO)與癲癇發作源起區有顯著的關聯，且被認可為一個可行的生理標記。為了促進HFO於癲癇術前/後評估使用，HFO的偵測與分類成了首要的課題，在本研究中建立了一套HFO自動化偵測演算法。在偵測處理時，先計算多通道統計值以保留通道的結構與防止資訊的遺失，隨後利用雙成分高斯混合模型設定閥值，避免閥值設定過高的問題。偵測到之可能HFO事件(plausible, pHFO)接著進行種類分類。依據原始數據、濾波數據和時頻圖的資訊將其分為(1)HFO、(2)癲癇棘波以及(3)癲癇棘波伴隨HFO三種事件種類，藉此與因濾波癲癇棘波產生的偽震盪區分。運用模擬數據來進行驗證的結果顯示，我們所提出的演算法能達到良好的靈敏度與陽性預測值表現。此外，我們也將這些演算法應用於3位患者的顱內腦電波數據，來試驗多通道偵測演算法的實行性。

Epilepsy is the most common neurological disease that affects people of all ages. For around 30% of epileptic patients, seizures are poorly controlled only with medication, and removing the brain region responsible for seizure onset is an alternative option. Recent researches indicate that high-frequency oscillations (HFOs) between 80 and 500 Hz are clearly linked to the seizure onset zone, which suggests HFO be a promising biomarker of epileptic tissue. To propel the clinical practice of HFOs, it is essential to detect them on intracranial electroencephalogram. In this study, several multi-channel algorithms for HFO determination are proposed. The detection process in the proposed scheme starts by calculating multi-channel statistics that allow the interrelations among the electrodes to be retained. On which, the detection thresholds are set epoch by epoch through a two-component Gaussian mixture model to avoid the threshold over-estimation problem. The events detected to be plausible HFOs (pHFOs) are subjected to a further classification such that the oscillations caused by filtering sharp transients can be isolated. By simultaneously considering the information of the raw data, filtered data and their time-frequency maps, all the pHFO events are ultimately categorized to be the events of HFOs, spikes, and spikes with HFOs. Experimental results using simulated iEEG recordings indicate that the proposed scheme achieves promising sensitivity and precision under various noise levels. Finally, the records from three epileptic patients are included to demonstrate their practicality.

摘要 I
ABSTRACT II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VII
第一章 緒論 1
1.1 癲癇 1
1.2 高頻震盪 1
1.3 研究動機 2
1.4 研究概要 5
第二章 研究數據及前處理 7
2.1 病患及臨床數據 7
2.2 數據前處理 8
2.2.1 電力線干擾消弭 8
2.2.2 濾波處理 10
第三章 自動化偵測演算法 12
3.1 單通道偵測器回顧 12
3.1.1 方均根偵測演算法 12
3.1.2 線長偵測演算法 13
3.2 pHFO偵測 15
3.2.1 多通道統計值 15
3.2.2 自適性閥值設定方法 17
3.3 pHFO分類 19
3.3.1 是否有癲癇棘波 21
3.3.2 濾波數據上是否有適當的震盪數量 21
3.3.3 癲癇棘波上是否有震盪 22
3.3.4 時頻圖上是否有高頻獨立成分 23
第四章 演算法性能評比 28
4.1 模擬數據 28
4.2 臨床數據目視檢測 31
4.3 評比指標 33
第五章 研究結果與討論 36
5.1 模擬數據偵測結果 36
5.2 臨床數據偵測結果 42
第六章 結論 47
參考文獻 48

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