摘要翻译：
伪影检测(AD)技术通过在临床事件检测(CED)和参数推导(PD)之前评估数据质量，最大限度地减少伪影对重症监护病房(CCU)获得的生理数据的影响。这篇方法综述介绍了独特的分类法来综合80多个基于以下六个主题的AD算法：（1）CCU；（2）生理数据来源；（3）收获的数据；（4）数据分析；（5）临床评价；（6）临床实施。综述结果表明，大多数已发表的算法：(a)是针对一种特定类型的CCU设计的；(b)仅根据从一个原始设备制造商(OEM)监视器收集的数据进行验证；(c)生成尚未正式确定的信号质量指标(SQI)，以便在临床工作流程中进行有用的集成；(d)以独立模式运作，或与CED或PD应用程序配合运作；(e)很少进行实时评价；和(f)在临床实践中没有实施。总之，建议AD算法符合具有共同定义数据的通用输入和输出接口：（1）类型；（2）频率；（3）长度；和(4)SQIs。这将促进(a)算法跨不同CCU域的可重用性；(b)对不同OEM监测数据的评价；(c)通过正式SQIs进行公平比较；(d)与其他AD、CED和PD算法有意义的集成；以及(e)在临床工作流程中的实时实施。
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英文标题：
《Implementation of Artifact Detection in Critical Care: A Methodological
  Review》
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作者：
Shermeen Nizami, James R. Green, and Carolyn McGregor
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Artifact Detection (AD) techniques minimize the impact of artifacts on physiologic data acquired in Critical Care Units (CCU) by assessing quality of data prior to Clinical Event Detection (CED) and Parameter Derivation (PD). This methodological review introduces unique taxonomies to synthesize over 80 AD algorithms based on these six themes: (1) CCU; (2) Physiologic Data Source; (3) Harvested data; (4) Data Analysis; (5) Clinical Evaluation; and (6) Clinical Implementation. Review results show that most published algorithms: (a) are designed for one specific type of CCU; (b) are validated on data harvested only from one Original Equipment Manufacturer (OEM) monitor; (c) generate Signal Quality Indicators (SQI) that are not yet formalised for useful integration in clinical workflows; (d) operate either in standalone mode or coupled with CED or PD applications; (e) are rarely evaluated in real-time; and (f) are not implemented in clinical practice. In conclusion, it is recommended that AD algorithms conform to generic input and output interfaces with commonly defined data: (1) type; (2) frequency; (3) length; and (4) SQIs. This shall promote (a) reusability of algorithms across different CCU domains; (b) evaluation on different OEM monitor data; (c) fair comparison through formalised SQIs; (d) meaningful integration with other AD, CED and PD algorithms; and (e) real-time implementation in clinical workflows.
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PDF链接：
https://arxiv.org/pdf/1805.00086