Biomedical Signal Sensing and Analysis

用于生物医学信号采集的有线和无线传感器在长期健康监测和远程健康应用中已变得无处不在。结合了信号处理和机器学习的生物医学信号分析正在成为一个重要的领域，无论是对医院仪器设备还是对家庭医疗设备都是如此。尽管生物医学仪器设备多年来得到了改进，并被广泛用于医疗保健和研究，但两个主要挑战仍然存在：1)对采集的信号进行稳健的分析和解释，以及2)设备的功率要求。目前的生物医学设备耗电量大，特别是在使用可穿戴设备进行长期健康监测时，最终会变得低效和不合规。造成这种情况的主要原因之一是从信号采集到分析和决策的生物医学信号处理算法的使用和设计效率低下。在提出的研究中，压缩传感(CS)技术将被系统地利用与生物医学信号相关的冗余和稀疏特性，来设计低功耗和健壮的数据采集系统。将寻求使用CS来分析和解释信号的新方向。建议的联合压缩感知和分析(CSA)框架将研究专门适用于生物医学信号的信息理论和优化方法，以处理信号的可变性(由于非平稳和非线性)、生理系统之间表现出的相互关系和相互作用，以及特定领域的生理伪像和干扰。这些新开发的CSA技术的性能测量将有助于提高用于长期监测应用的生物医学传感器的功率和带宽要求，并将提供稳健和高效的信号显著特征提取，以改进医疗保健决策。这项研究的应用将极大地有助于心脏信号分析、神经肌肉信号分析、呼吸信号分析以及低功耗可穿戴设备的健康和健康设计。这里开发的技术和算法还可以扩展和适应其他新兴领域，如生物识别/网络安全、雷达/声纳以及与多媒体有关的信号处理和机器学习。

Wired and wireless sensors for biomedical signal acquisition have become ubiquitous for long-term health monitoring and tele-health applications. Biomedical signal analysis which combines the aspects of signal processing and machine learning is becoming an area of importance, both for hospital-based instrumentation and for home-based medical devices. While biomedical instrumentation has improved over the years and is commonly used in health care and research, two main challenges still persist: 1) robust analysis and interpretation of the signals that are acquired, and 2) power requirements of the devices. The current biomedical devices consume large amount of power, and eventually become inefficient and non-compliance especially in long term health monitoring using wearable devices. One of the main reasons for this has been the inefficient use and design of biomedical signal processing algorithms right from signal acquisition to analysis, and decision making. In the proposed research, compressive sensing (CS) technique in which redundancy and sparsity characteristics associated with a biomedical signal are systematically exploited, will be used in designing low-power and robust data acquisition system. A new direction of using CS for analysis and interpretation of the signals will be pursued. The proposed joint compressive sensing and analysis (CSA) framework will look into information theory and optimization methods that are specifically adapted for biomedical signals to handle the variability (due to non-stationarity and non-linearity) of the signals, the inter-relationships and interactions exhibited among physiological systems, and the domain-specific physiological artifacts and interferences. The performance measures of these newly developed CSA techniques will help in improving the power and bandwidth requirements of biomedical sensors used in long term monitoring applications, and will provide robust and efficient extraction of salient features from the signals for improved decision making in healthcare. The applications of the proposed research will tremendously benefit the areas of cardiac signal analysis, neuromuscular signal analysis, respiratory signal analysis, and design of low-power wearables for health and wellness. The techniques and algorithms developed here could also be extended and adapted to other emerging areas such as biometrics/cybersecurity, RADAR/SONAR, and multimedia related signal processing and machine learning.