Biomedical Signal Sensing and Analysis

生物医学信号传感与分析

• 批准号: RGPIN-2020-04628
• 负责人: Krishnan, Sridhar
• 金额: $ 2.84万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716649
• 关键词: Biomedical; Signal; Sensing; Analysis

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.

生物医学信号获取的有线和无线传感器已无处不在，用于长期健康监测和远程医疗应用。结合信号处理和机器学习方面的生物医学信号分析已成为基于医院的仪器和基于家庭的医疗设备的重要性领域。尽管生物医学仪器多年来有所改善，并且通常用于医疗保健和研究中，但仍有两个主要挑战持续存在：1）对获得的信号的强大分析和解释，以及2）设备的功率要求。当前的生物医学设备会消耗大量功率，并最终变得效率低下且不合规，尤其是在使用可穿戴设备的长期健康监测中。这样做的主要原因之一是从信号获取到分析和决策的生物医学信号处理算法的效率低下和设计。在拟议的研究中，系统利用与生物医学信号相关的冗余和稀疏特性的压缩感应（CS）技术将用于设计低功率和强大的数据采集系统。 将追求使用CS分析和解释信号的新方向。提出的联合压缩感应和分析（CSA）框架将研究信息理论和优化方法，这些方法专门针对生物医学信号，以处理信号的可变性（由于非平稳性和非线性性），在生理系统以及领域的生理系统，以及特定的生理学方法和互动之间表现出的相互关联和交互。这些新开发的CSA技术的性能度量将有助于改善长期监控应用中使用的生物医学传感器的功率和带宽要求，并将提供强大而有效地从信号中提取显着特征，以改善医疗保健中的决策。拟议的研究的应用将极大地使心脏信号分析，神经肌肉信号分析，呼吸信号分析以及低功率可穿戴设备的设计方面有益于健康和健康。这里开发的技术和算法也可以扩展并适应其他新兴领域，例如生物识别/网络安全，雷达/声纳和相关的信号处理和机器学习。