本次逸奇科技與中央大學-數學與理論物理中心所合作舉辦「生醫訊號與時頻分析研討會」，邀請到多位學者為大家帶來相關研究成果的發表，我們也很高興的看到超過150名的參加者前來共襄盛舉。相關內容及教材下載連結如下:

活動議程 (依演講順序排列):

1.HHT: the theory, implementation and application 投影片下載
王逸民　博士 / 逸奇科技總經理
什麼是頻率？是週期的倒數，是一段時間發生的次數？還是傅立葉頻譜的數值？
什麼是瞬時頻率？照測不準原理，根本沒有這個觀念。那時頻分析所呈現頻率隨著時間變化的現象，瞬時頻率的表現如何解釋？是否不同頻率的定義有不同的解釋。
要改善這些問題，頻率的定義必需要有更基本的檢視！
黃鍔院士的 「赫伯特－黃變換法」 ( Hilbert-Huang Transformation ，簡稱 HHT ) ，採用 Hilbert 對瞬時頻率的定義，比較傅利葉分析，在處理非線性的問題上，有許多好處。 HHT 訊號處理法完全改變以往對於非線性、非穩態訊號幾乎束手無策的窘境。他跳脫出傳統數學理論的限制，因此運用 HHT 進行分析而得到的結論亦顛覆了傳統數學的思維。 HHT 在被提出後短短幾年內就被廣泛應用於科學、醫學、工程、社會、人文科學不同領域的數據分析，它的廣泛應用也導致一個革命性數據分析方法的演進。

2.Investigating complex patterns of blocked intestinal artery blood pressure signals by empirical mode decomposition and linguistic analysis 投影片下載
謝建興　教授 / 元智大學 機械工程系
In this investigation, surgical operations of blocked intestinal artery have been conducted on pigs to simulate the condition of acute mesenteric arterial occlusion. The empirical mode decomposition method and the algorithm of linguistic analysis were applied to verify the blood pressure signals in simulated situation. We assumed that there was some information hidden in the high-frequency part of the blood pressure signal when an intestinal artery is blocked. The empirical mode decomposition method (EMD) has been applied to decompose the intrinsic mode functions (IMF) from a complex time series. But, the end effects and phenomenon of intermittence damage the consistence of each IMF. Thus, we proposed the complementary ensemble empirical mode decomposition method (CEEMD) to solve the problems of end effects and the phenomenon of intermittence. The main wave of blood pressure signals can be reconstructed by the main components, identified by Monte Carlo verification, and removed from the original signal to derive a riding wave. Furthermore, the concept of linguistic analysis was applied to design the blocking index to verify the pattern of riding wave of blood pressure using the measurements of dissimilarity. Blocking index works well to identify the situation in which the sampled time series of blood pressure signal was recorded. Here, these two totally different algorithms are successfully integrated and the existence of the existence of information hidden in high-frequency part of blood pressure signal has been proven.

3.Detecting hidden information in ventricular fibrillation Background 投影片下載
蕭又新 副教授 / 政治大學 應用物理所
It is well known that the state of rhythm during resuscitation determines medical treatments.In particular, the study of pathophysiological processes of ventricular fibrillation (VF) has attracted considerable interest. To gain insight during the period of VF, various approaches,including instantaneous frequency measures in real time-domain as well as fluctuation analyses,are used to analyze real-life data.
Materials and Methods:
The surface ECG recordings from 35 VF patients were obtained from the physionet public website(www.physionet.org/physiobank/database). Two instantaneous frequency measures are used to study the VF signal. One is the Morlet transform method (MTM) and the other is the empirical mode decomposition (EMD). In addition, a popular approach in analyzing the nonstationary time-series, i.e., detrended fluctuation analysis (DFA), is used to detect statistical characteristics of the VF signal.
Results:
Both MTM and EMD exhibit the similar dominant frequency (DF) in a real time display.
However, some of extracted details are different in these two approaches.
DFA reveals that many VF patients display the uncorrelated property in long-term fluctuations,which indicates the stochastic factor would be essential for the development of VF.
In summary, these results suggest that pathophysiological processes during the period of VF should be from two independent sources at least. One of them can generate DF and its associated harmonic frequencies, thus, the coherent behavior is expected.The other has a tendancy to destruct the coherent behavior and finally makes a complicated manner of the VF signal.

4.Visualizing the Physical Phenomena for Computational Science 投影片下載
陳立格 / 逸奇科技 工程師
動態繪圖於科學計算是不可獲缺的一環。唯將計算過程儲存，再以後處理軟體製做動畫，實過於繁。更甚者，此種做法於程式除錯極其麻煩，欲以幾張靜態圖型探索其中科學奧密，並發掘不合理處，所耗之工與成過極易不成正比。
現在，以些物理問題為對像，讓大家體會Matfor 在實做科學計算之方便性。介由Matfor的幫助，原來物理之美，就是離你我如此接近。

5.腦波與心電訊號之分析與判讀 投影片下載
李政杰 / 國立台灣大學 機械所
ECG俗稱心電圖，藉由觀察心電圖之波形和頻率規律性，可檢查受測者有無各種心臟疾病，亦可了解受測者心臟的健康狀況。生理訊號是用來檢測生理機能的重要工具。量測因生理反應產生之訊息，經由某些方式判讀和分析，可推測個人的身體概況。

EEG俗稱腦波，是藉由量測頭皮表面的電位變化得到的生理訊號。在應用上，EEG常被用來評估大腦機能和精神狀態，例如：睡眠狀態檢測、腦部疾病檢測、疲勞狀態檢測、壓力檢測等。

Visual Signal 軟體試用下載 - 進行時頻分析訊號處理，毋需撰寫程式

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