Biomedical Monitoring, Signal Processing and Control

尽管自动化技术已经在所有现代工程系统和设备中无处不在，比如无人驾驶汽车的最新发展，但它尚未在医疗保健领域发挥重要作用。控制技术目前在该部门缺乏影响的众多原因包括以下挑战：1)患者之间的显著差异，例如他们对特定药物的反应方式；Ii)开发无创、可靠和易于使用的传感器的困难；Iii)以及使用自动化的，在某种程度上自主的，例如管理潜在危险药物时的安全问题。然而，最近出现的闭环植入式心脏除颤器和1型糖尿病的闭环血糖控制，如fda批准的美敦力MiniMED 670G系统，每5分钟调整一次基础胰岛素，一个自动化、个性化医疗保健的时代即将到来。在过去的17年里，我一直处于这种技术的前沿，因为我的工作重点是为临床医生提供基于信号处理和控制的工具，以帮助改善患者的治疗结果和安全，不仅在重症监护环境中，而且在全球卫生环境中。本提案是这项工作的延续，重点关注麻醉和镇静安全自动给药的传感、信号处理和控制技术。在这个项目中我将：******1。使用一些创新的控制方法，为麻醉和镇静的安全自动给药开发稳健和安全的控制算法***2。发展无创传感技术，例如心血管系统的光电容积脉搏波描记术（PPG）；更高效的脑电图（EEG）技术，能够检测与先兆偏头痛和某些类型癫痫相关的极低频、大振幅活动；近红外光谱（NIRS）用于大脑和组织监测*** 2。开发有效的信号处理技术来提取生理参数中的临床相关特征，例如PPG和EEG******作为这项工作的结果，麻醉和镇静自动化系统应该成为标准护理，从而减少手术室和重症监护病房的不良事件，并为患者提供更好的长期预后，如减少术后谵妄和认知障碍。新的脑电图技术可能导致1)更好地了解偏头痛，潜在地导致更好的预防和治疗2)更好地预测和定位癫痫发作的焦点。将脑电图与近红外光谱相结合将有助于更好地了解新生儿重症监护病房早产儿的疼痛感知，并找到更好的方法来避免和管理疼痛。最后，信号处理方面的工作应该导致从可穿戴设备获得的数据中提取临床相关信息的创新方法。*****

Although automation technology has become ubiquitous in all of modern engineered systems and devices, see for instance current developments in driverless cars, it has yet to play an essential role in healthcare. Among the many reasons for the current lack of impact of control technology in that sector are the challenges posed by i) the significant variability among patients, e.g. in the way they respond to a given drug; ii) the difficulty in developing non-invasive, reliable and easy-to-use sensors; iii) and the safety concerns when using an automated, somewhat autonomous that e.g. administers potentially dangerous drugs. However, the recent advent of closed-loop implantable cardiac defibrillators and closed-loop control of blood glucose for type-1 diabetes, such as the FDA-approved Medtronic MiniMED 670G system that adjusts basal insulin every 5 min, an era of automated, personalized healthcare is about to usher. For the last 17 years, I have been at the forefront of such technology, as my work has focused on providing clinicians tools based on signal processing and control to help improve patient outcomes and safety not only in critical care settings but also in global health settings. This proposal is a continuation of this work with a strong focus on sensing, signal processing and control techniques for safe automatic drug delivery for anesthesia and sedation. Within this program I will:******1. Develop robust and safe control algorithms for safe automated drug delivery for anesthesia and sedation using a number of innovative control methodologies ***2. Develop noninvasive sensing techniques, e.g. photoplethysmography (PPG) for the cardiovascular system; more performant electroencephalography (EEG) technology capable of detecting very low frequency, large amplitude activity associated with migraines with aura and certain types of epilepsy; near-infrared spectroscopy (NIRS) for brain and tissue monitoring***3. Develop efficient signal processing techniques to extract clinically-relevant features in physiological parameters, e.g. the PPG and EEG******As a result of this work, automated systems for anesthesia and sedation should become standard of care, resulting in reduced adverse events in operating rooms and intensive care units and better long term outcomes for patients such as reduction in post-operative delirium and cognitive impairment. Novel EEG technology could result in i) better understanding of migraines, potentially leading to better prevention and treatment ii) better way to predict and localize the focus of epileptic seizures. Combining EEG with NIRS will lead to a better understanding of pain perception and better ways to avoid and manage it in preterm infants during their stay in the neonatal intensive care unit. Finally, work on signal processing should lead to innovative ways to extract clinically relevant information from data obtained from wearables.*****