過去的研究指出交感神經系統(Sympathetic Nervous System)的過度活化與心律不整有關。但傳統的交感神經訊號量測方式必需使用侵入式的電極量測，除了需要專業人員操作，也沒辦法長時間量測，近期由高取樣率心電圖訊號進行皮膚交感神經訊號(Skin Sympathetic Nerve Activity, SKNA)分析的新技術。發現在心房顫動發生前SKNA訊號和星狀神經節活動有著相似的活動振幅的增加。由此可知SKNA訊號有高度潛力可替代傳統的自律神經量測方式。 目前已知交感神經系統活性變化和心房顫動的發生有關，我們便想證明由SKNA提取的參數能否對心房顫動電燒後是否復發具有預測性。同時我們也想了解SKNA是否能用來分辨心臟衰竭患者的嚴重程度。因此我們使用了三組不同的資料庫去做分析，分別是患有心房顫動之患者經過電燒治療後復發與否的組;患有心臟衰竭以及糖尿病之患者，吃下兩種不同種類的糖尿病藥物後的結果;患有心臟衰竭之患者，進入加護病房後，能活著離開的患者及不幸離世的患者。 以往的SKNA訊號的分析方法是將SKNA經過一系列處理成aSKNA後，透過設定閥值去定義出burst以及basleine後做活化分析(Burst analysis)，來量化交感神經活動的動態變化。在觀察活化分析的結果後發現，其中burst的持續時間是最關鍵的係數。因此我們新增了針對aSKNA的時序複雜度分析的方法，用來分析burst在時間上的轉換模式。 ;Past studies have indicated a correlation between excessive activation of the Sympathetic Nervous System (SNS) and arrhythmias. However, traditional methods for measuring sympathetic nerve signals require invasive electrode measurements, necessitate skilled personnel, and lack the capability for prolonged monitoring. A recent advancement involves the analysis of Skin Sympathetic Nerve Activity (SKNA) using high-sampling-rate electrocardiogram signals. It has been discovered that the increase in activity amplitude of SKNA signals prior to atrial fibrillation occurrence is similar to that of stellate ganglion activity. This suggests significant potential for SKNA signals to replace conventional autonomic nervous system measurement methods. Given the known association between sympathetic nervous system activity variation and atrial fibrillation occurrence, our aim is to ascertain whether parameters extracted from SKNA can predict post-electroablation atrial fibrillation recurrence. Additionally, we seek to determine if SKNA can differentiate the severity of heart failure in patients. To achieve this, we conducted analyses using three distinct databases: patients with atrial fibrillation who underwent electroablation therapy to assess recurrence; patients with heart failure and diabetes who were administered different diabetes medications; patients with heart failure admitted to the intensive care unit, distinguishing survivors from non-survivors. Previous SKNA signal analysis involved processing SKNA into a derived form called aSKNA. Burst and baseline were defined using threshold settings, and Burst analysis was performed to quantify dynamic changes in sympathetic nerve activity. Notably, the duration of bursts emerged as a critical coefficient based on Burst analysis results. Consequently, we introduced a method for analyzing the temporal complexity of aSKNA, aimed at studying the transition patterns of bursts over time.
