勇于冒險 甘于艱苦 樂于和諧

一、活動介紹

為鍛煉生物醫學工程系學生的科研展示能力，促進學術交流與合作，由生物醫學工程系主辦，生物醫學工程系第二黨支部承辦的BME研究生學術沙龍火熱拉開帷幕。該活動計劃每月舉行一次，每次由我系兩個課題組的研究生或博士后進行學術分享。

三、活動流程

17:00-17:40? Normal talk

17:40-17:50? Short talk

18:00-18:30? 活動閉幕及交流討論

三、本期活動預告

【Normal talk】

肖靖雨（2019級博士生，郭瓊玉課題組）

題目：體外再細胞肝臟模型在肝癌栓塞治療，光熱療法以及免疫治療中的應用?

報告摘要：

體外再細胞肝臟模型是在脫細胞大鼠肝臟的基礎上圍繞門靜脈三聯組回輸大小和位置均可控的HepG2細胞集群獲得的。這種模型保留了肝臟血管系統和細胞外基質的復雜結構，并同時通過肝靜脈血管維持營養和氧氣供應的動態生理環境，與肝癌獨特的腫瘤微環境非常相似。當細胞回輸完成后可以通過門靜脈血管栓塞，光熱及免疫殺傷進行治療，有望對肝癌治療策略進行有效模擬和評估。

報告時間：8月25日，17:00-17:20

楊膺琨（2018級博士生，陳放怡課題組）

報告題目：PDT精準損傷前庭器官治療頑固性眩暈的方法研究

報告摘要：

頑固性前庭眩暈主要是前庭系統過度敏感或病變造成的。現有的臨床治療手段包括外科手術和局部注射耳毒性藥物慶大霉素，通過損傷部分前庭感受器（毛細胞），來降低其敏感度從而減輕和控制眩暈癥狀。然而，外科手術的損傷較大，耳毒性藥物則會在損傷前庭器官的同時也損傷鄰近的聽覺器官耳蝸而造成聽力下降。由于光動力療法有良好的時間和空間精確性，本課題將其應用于前庭器官的精確損傷。通過本課題組研發的小鼠前庭功能量化設備，我們顯示了PDT可以實現定量地損傷小鼠不同部位的前庭器官，并且避免損傷聽力。本研究提出了一種新型精準治療頑固性眩暈的思路，并提供了臨床前實驗基礎。

報告時間：9月28日，17:20-17:40

【Short talk】

報告題目：

Surveillance Camera-based Cardio-respiratory Monitoring for Critical Patients in ICU

報告摘要：

Camera-based vital signs monitoring has been extensively researched in non-medical fields in recent years. Intensive Care Unit (ICU) typically requires continuous monitoring of patients' physiology for alarming the emergency such as patient deterioration or delirium. In this paper, we propose to use the surveillance closed-circuit television (CCTV) cameras installed in ICU for cardio-respiratory monitoring of critically-ill patients, thus created a first clinical video dataset (including 10 deteriorated patients) in ICU using CCTV cameras. Along with the dataset, a video processing framework with the latest core algorithms designed for pulse and respiratory signal extraction has been demonstrated. A joint Region-of-Interest optimization approach using pulsatile living-skin maps and respiratory maps was proposed to improve the vital signs monitoring for ICU patients. A motion intensity based quality metric was designed to reject measurement outliers induced by patient motion or nurse operation. Based on the valid measurements selected by the metric, the overall Mean Absolute Error for heart rate is 1.7 bpm, and for breathing rate is 1.6 bpm. Preliminary clinical validations show that robust cardio-respiratory monitoring is indeed feasible for CCTV cameras in ICU, and such a warding solution can be quickly integrated into current hospital information systems for large-scale deployment, by leveraging the existing hardware and infrastructures of the Internet of Medical Things.

報告時間：9月28日，17:40-17:45

報告題目：

A Multi-modal Clinical Dataset for Critically-ill and Premature Infant Monitoring: EEG and Video

報告摘要：

The comprehensive monitoring of cardio-respiratory and neurological events of premature infants is desired for the Neonatal Intensive Care Unit (NICU). Video-based infant monitoring is an emerging tool for NICU as it eliminates skin irritations and enables new measurements like pain assessment. A multi-modal clinical dataset across the measurement of EEG and videos will be helpful in developing novel monitoring solutions for infant care. In this paper, we created such a dataset by simultaneously collecting the EEG signals and videos data from critically ill and preterm infants in NICU. Along with the recordings, we used the video-based cardio-respiratory measurements (heart rate and respiratory rate) to examine the validity of video recordings. We employed a classical video-based physiological measurement framework called Spatial Redundancy in combination with living-skin detection to measure the vital signs of recorded infants. The pilot measurements show the feasibility as well as the challenges that need to be addressed in algorithmic design in the next step. The dataset will be made publicly available to facilitate the research in this area. It will be useful for studying the video-based infant monitoring and its fusion with EEG, which may lead to new measurements such as a neonatal PSG for infant sleep staging and disease analysis (e.g. neonatal encephalopathy, neonatal respiratory distress syndrome).

報告時間：9月28日，17:45-17:50

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