Biomedical Monitoring, Signal Processing and Control

尽管自动化技术已在所有现代工程系统和设备中无处不在，例如，看看无人驾驶汽车的最新发展，但它尚未在医疗保健中发挥关键作用。目前在该领域缺乏控制技术影响的许多原因包括：i)患者之间的差异很大，例如他们对某一药物的反应方式；ii)开发非侵入性、可靠和易于使用的传感器的困难；iii)使用自动化的、有点自主的、例如管理潜在危险药物的传感器时的安全问题。然而，最近出现的闭环式植入式心脏除颤器和1型糖尿病的血糖闭环控制，例如FDA批准的美敦力MiniMed 670g系统每5分钟调整一次基础胰岛素，自动化、个性化医疗保健的时代即将到来。在过去的17年里，我一直站在这类技术的前沿，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，以帮助改善重症监护环境中的患者结果和安全性，甚至在全球医疗环境中也是如此。这项提议是这项工作的继续，重点放在用于麻醉和镇静的安全自动药物输送的传感、信号处理和控制技术上。在这项计划中，我将：*1.使用一些创新的控制方法**2.开发用于麻醉和镇静的安全自动给药的健壮和安全的控制算法**2.开发非侵入性传感技术，例如心血管系统的光体积描记(PPG)；能够检测与有先兆的偏头痛和某些类型的癫痫相关的极低频率、大幅度活动的更高性能的脑电(EEG)技术；近红外光谱(NIRS)用于大脑和组织监测*3.开发有效的信号处理技术来提取生理参数中与临床相关的特征，例如PPG和EEG*由于这项工作的结果，麻醉和镇静的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结果，如减少术后精神错乱和认知障碍。新的脑电技术可能导致：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EEG和NIRS相结合将有助于更好地了解早产儿在新生儿重症监护病房期间的疼痛感受，并更好地避免和管理早产儿疼痛。最后，信号处理方面的工作应该带来创新的方法，从可穿戴设备获得的数据中提取临床相关信息。

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.*****