Progressive Acute Stroke Severity Monitoring and Atrial Fibrillation Detection Based on Multi-modal Analysis of Physiological Signals 以多項性生理訊號分析應用於 長期監控急性中風病患嚴重度與偵測心房顫動 Future Working Item Meeting 計畫期間： 105/10/01~107/09/30 PI:湯頌君醫師、吳安宇教授、賴達明醫師 Sep 30, 2016
Project Goal Stroke is the leading cause of mortality and morbidity Atrial fibrillation (AF) is a risk factor for ischemic stroke Aim of the study: Stroke severity monitoring and AF detection based on bio-signals in hospital Advantage: Real-time Continuous Inexpensive 中風為世界三大死因，根據統計，每年全世界有1500萬人罹患中風，其中大約五百萬人死亡，另外五百萬人永久失能，這會造成巨大的醫療照護成本。 如果可以早期預測中風患者的復原結果，就可以早點評估、積極治療，減少失能或死亡的發生。 生理訊號是連續的，又能即時反應出病患當下的生理狀況，醫療人員可以透過這些訊號，持續觀察並追蹤中風病患的狀況，甚至預測他們的復原結果。 此外，比起醫學影像系統，生理訊號還具有安全、Portable、低成本的優點。
Goal of First Year First half of the year Involve bio-signals of ICU for enhanced algorithm development & validation Utilize medical-grade sensors for recording and analyzing bio-signals PPG-based AF detection Test with non-stroke patients (test set) Refine the algorithm Stroke severity monitoring Outcome prediction => Progressive monitoring Second half of the year
Goal of Second Year Implementation and trial on MTK bio-platform Multi-modal analysis for progressive stroke severity monitoring PPG for AF detection PPG EKG EEG ABP