Biomedical signal quality analysis

生物医学信号质量分析

• 批准号: RGPIN-2014-04722
• 负责人: Chan, Adrian
• 金额: $ 1.82万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=647099
• 关键词: Biomedical; signal; quality; analysis

We are currently experiencing an explosive growth in data. This growth includes biomedical data (e.g., electrocardiogram (ECG), electromyogram (EMG), pulse oximetry, blood pressure) which provide valuable information regarding the status and function of the body and are useful in a variety of applications (e.g., health/wellness, biometrics, gaming, and sports/fitness). There exists a large and continually growing body of knowledge regarding the acquisition of biomedical signals, as well as signal processing methods to extract useful information. Biomedical signals, however, can be contaminated due to noise, artifacts, and measurement setup errors; this is particularly true in unsupervised setups (e.g., telehealth), where highly trained operators are not present. Contaminants in the recordings can lead to misinterpretations, inaccuracies, and errors, including misdiagnoses. Despite advances in biomedical instrumentation, contaminants are frequently present in recordings. Currently, biomedical signal quality analysis relies on human experts. This is time-consuming, costly, and prone to human error. In addition, the increases in pervasive, continuous and/or multi-channel monitoring are making manual or semi-automated biomedical signal quality analysis methods impractical due to the amount of data.**The objective of this research is to develop novel automated biomedical signal quality analysis methods to detect, identify, quantify, and mitigate contaminants. The proposed research is organized into three main research themes: 1) Multi-scale analysis, 2) Multi-variate analysis, and 3) Pattern recognition. Multi-scale approaches are well-suited to signals that arise from complex interconnected systems, such as biological systems. Recent research indicates strong potential in this approach, compared to conventional approaches that are either time or frequency based. Multi-variate approaches take advantage of redundant and complementary information within multi-channel recordings (i.e., multiple leads for the same signal type) and/or multi-modal recordings (i.e., recordings of different signal types). Pattern recognition methods can be employed to discover and leverage trends within the data; this can be used to detect and identify contaminants in biomedical signals, as well as classify the quality of data (e.g., excellent, good, poor, unacceptable). Methods will be evaluated in terms of performance (e.g., correctly detecting and identifying contaminations) and generalizability (e.g., methods work for various contaminants and combinations of contaminants).**The exponential growth in biomedical data is associated with various challenges (e.g., acquisition, transferring, storage, and visualization). This research tackles a key, under-researched, area of quality analysis. There is utility within the large datasets being developed, but the capacity to discern which data has adequate quality, and avoid overly contaminated data, is essential. Outcomes of the proposed research will provide engineering contributions in signal processing and data quality analysis, and in the long-term be applied in the context of biomedical signal instrumentation and measurement. For example, automatic biomedical signal quality analysis methods will enable acquisition setups to be validated, alerting operators of issues and directing them on how to resolve these issues. It will also improve the performance of signal processing methods that extract information from these signals (e.g., increase accuracy of clinical decision support systems, reduction of false alarms). While this research is focused on biomedical data, the concepts and frameworks developed in this research are applicable to other data types.

我们目前正在经历数据的爆炸式增长。这种增长包括生物医学数据（例如，心电图（ECG）、肌电图（EMG）、脉搏血氧测定法、血压），其提供关于身体状态和功能的有价值的信息并且可用于各种应用（例如，健康/保健、生物测定、游戏和运动/健身）。关于生物医学信号的获取以及提取有用信息的信号处理方法，存在大量且不断增长的知识。然而，由于噪声、伪影和测量设置误差，生物医学信号可能被污染;这在无监督设置中尤其如此（例如，远程保健），但没有训练有素的操作人员在场。记录中的污染物可能导致误解、不准确和错误，包括误诊。尽管在生物医学仪器的进步，污染物经常出现在记录。目前，生物医学信号质量分析依赖于人类专家。这是耗时的，昂贵的，并且容易出现人为错误。此外，由于数据量大，普遍、连续和/或多通道监测的增加使得手动或半自动生物医学信号质量分析方法变得不切实际。**本研究的目的是开发新的自动化生物医学信号质量分析方法，以检测，识别，量化和减轻污染物。本研究主要分为三个研究主题：1）多尺度分析，2）多变量分析，3）模式识别。多尺度方法非常适合于从复杂的互连系统（如生物系统）中产生的信号。最近的研究表明，与基于时间或频率的传统方法相比，这种方法具有强大的潜力。多变量方法利用多通道记录内的冗余和互补信息（即，相同信号类型的多个导联）和/或多模式记录（即，不同信号类型的记录）。可以采用模式识别方法来发现和利用数据内的趋势;这可以用于检测和识别生物医学信号中的污染物，以及对数据的质量进行分类（例如，优秀、好、差、不可接受）。将根据性能对方法进行评价（例如，正确地检测和识别污染物）和普遍性（例如，方法适用于各种污染物和污染物的组合）。**生物医学数据的指数增长与各种挑战相关联（例如，获取、传输、存储和可视化）。这项研究解决了一个关键的，研究不足的质量分析领域。正在开发的大型数据集具有实用性，但识别哪些数据具有足够的质量并避免过度污染的数据的能力至关重要。拟议的研究成果将提供信号处理和数据质量分析的工程贡献，并在长期的生物医学信号仪器和测量的背景下应用。例如，自动生物医学信号质量分析方法将使采集设置得到验证，提醒操作员问题并指导他们如何解决这些问题。它还将提高从这些信号中提取信息的信号处理方法的性能（例如，增加临床决策支持系统准确性，减少假警报）。虽然这项研究的重点是生物医学数据，但在这项研究中开发的概念和框架适用于其他数据类型。