基于神经条件随机场的结构化学习方法 睡眠分期

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摘要翻译：
睡眠对人的身心健康都起着至关重要的作用。随着肥胖等因素的迅速增加，像睡眠呼吸暂停这样的睡眠障碍越来越普遍。睡眠呼吸暂停最常见的治疗方法是持续正压通气(CPAP)疗法。然而，目前还没有监测CPAP患者进展的机制。从CPAP血流信号中准确地检测睡眠阶段对这一机制至关重要。我们首次提出了一个仅基于流量信号的自动睡眠分期模型。最近，深度神经网络通过消除手工制作的特征，在睡眠分期方面显示出了很高的准确性。然而，这些方法只关注于从输入信号中提取信息特征，而没有注意到输出序列中睡眠阶段的动态变化。我们提出了一个端到端的框架，该框架结合深度卷积和递归神经网络从原始流量信号中提取高级特征，并基于条件随机场建立结构化的输出层来建模睡眠阶段的时间转移结构。我们使用我们的模型对以前的方法进行了10%的改进，这可以扩展到以前的睡眠分期深度学习方法。我们还表明，我们的方法可以用于准确跟踪睡眠指标，如从睡眠阶段计算的睡眠效率，这些指标可以用于监测CPAP治疗对睡眠呼吸暂停患者的反应。除了技术上的贡献，我们期望这项研究能激发睡眠科学中新的研究问题。
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英文标题：
《A Structured Learning Approach with Neural Conditional Random Fields for
  Sleep Staging》
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作者：
Karan Aggarwal, Swaraj Khadanga, Shafiq R. Joty, Louis Kazaglis,
  Jaideep Srivastava
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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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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
--

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英文摘要：
  Sleep plays a vital role in human health, both mental and physical. Sleep disorders like sleep apnea are increasing in prevalence, with the rapid increase in factors like obesity. Sleep apnea is most commonly treated with Continuous Positive Air Pressure (CPAP) therapy. Presently, however, there is no mechanism to monitor a patient's progress with CPAP. Accurate detection of sleep stages from CPAP flow signal is crucial for such a mechanism. We propose, for the first time, an automated sleep staging model based only on the flow signal. Deep neural networks have recently shown high accuracy on sleep staging by eliminating handcrafted features. However, these methods focus exclusively on extracting informative features from the input signal, without paying much attention to the dynamics of sleep stages in the output sequence. We propose an end-to-end framework that uses a combination of deep convolution and recurrent neural networks to extract high-level features from raw flow signal with a structured output layer based on a conditional random field to model the temporal transition structure of the sleep stages. We improve upon the previous methods by 10% using our model, that can be augmented to the previous sleep staging deep learning methods. We also show that our method can be used to accurately track sleep metrics like sleep efficiency calculated from sleep stages that can be deployed for monitoring the response of CPAP therapy on sleep apnea patients. Apart from the technical contributions, we expect this study to motivate new research questions in sleep science.
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PDF链接：
https://arxiv.org/pdf/1807.09119

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关键词：学习方法 结构化 Applications Optimization Contribution methods 呼吸 based 监测 分期