演講公告
新聞標題： ( 2015-05-26 )
演講主題：【轉知】20150601 交通大學數學建模與科學計算研究中心學術演講
主講人：
演講日期：2015年06月01日
演講地點：科學一館307室
摘要內容：
more detail please see the
Speaker 1：
Dr. Yu-Ting Lin (Department of Anesthesiology, Shin Kong Wu Ho-Su Memorial Hospital)
Title: The Modeling and Quantification of Rhythmic to Non-rhythmic 13:30-14:20pm
Speaker 2：
Prof. Hau-tieng Wu (Department of Mathematics, University of Toronto)
Title: When manifold learning and time frequency analysis meet in medicine 14:30-15:20pm
Abstract 1：
Variations of instantaneous heart rate appear regularly
oscillatory in deeper levels of anesthesia and less regular in lighter levels of anesthesia. It is impossible to observe this
“rhythmic-to-non-rhythmic” phenomenon from raw electrocardiography waveform in current standard anesthesia monitors. To explore the possible clinical value, I proposed the adaptive harmonic model, which fits the descriptive property in physiology, and provides adequate mathematical conditions for the quantification. Based on the adaptive harmonic model,
multitaper Synchrosqueezing transform was used to provide time-varying power spectrum, which facilitates to compute the quantitative index:
“Non-rhythmic-to-Rhythmic Ratio” index (NRR index). I then used a clinical database to analyze the behavior of NRR index and understand its clinical value by comparing with other standard indices of anesthetic depth.
Abstract 2：
Explosive technological advances lead to current and
future exponential growth of massive data-sets in medicine. To better understand such “big data” in the new era, we need innovations in data analysis. Of particular importance is adaptive acquisition of essential features and information hidden in the massive data-sets, for example, the hidden low dimensional dynamics hidden inside the high dimension data, the time-varying periodicity and trend intrinsic to the system. In addition,
the robustness of the algorithm to different noises and computational efficiency should be taken care. In this presentation, I will show how to combine two modern adaptive signal processing techniques, diffusion maps and synchrosqueezing transform, to meet such needs. We will discuss direct application of our solution the sleep-depth detection problem from the polysomographic signal.
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