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：

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.