結論 - 改良式本質模態分解法在訊號處理之應用

5.1 本論文之貢獻

基於EMD之演算法與Wavelets皆被歸類為時間─尺度分解法，此種分解法的特性為可以同時兼顧時間及頻率的解析度，基本的方法為：

1) 將原始訊號拆解成高頻及低頻的部分；

2) 保留高頻部分，將低頻部分繼續拆解成相對高頻及相對低頻部分；

3) 重複 2)，直到訊號符合某些終止條件。

本論文透過大量文獻回顧，歸納並比較 EMD 演算法之三大課題的改進方式：停止準則、包絡線與邊界效應；並提出改良式本質模態分解法(RIMD) 之構想。透過八組模擬訊號的試驗結果，發現基於EMD 之演算對於具有趨勢訊號分量的拆解能力特別突出；這與EMD 將原始訊號分解成有限個 IMFs 與一個均值趨勢分量加總之特性非常符合。最後，利用兩組基準線飄移心電訊號測試，結果顯示RIMD 皆能將低頻的趨勢訊號分解出來，移除後便能達到雜訊濾除的成果。

5.2 未來展望

在RIMD 中值點的選取上，一樣存在著邊界效應的問題。雖然 RIMD 在邊界極值點的選取上，只需要考慮一次，但由立方雲線聯結中值點所構成的包絡線均值還是有可能產生訊號振盪，並且在迭代的篩程序中造成訊號失真的現象。如何改善這類的問題，是持續努力的方向。

並且在邊界中值點係數α 的選擇上，如何經由演算法自行判斷，降低使 用者選取所得到的誤差，也是需要改善的重點之一。

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