[电气工程与系统科学] 基于CNN的多模态数据的鲁棒性心跳检测 广义信息融合 [推广有奖]

摘要翻译：
目的：心跳检测仍然是心脏病诊断和治疗的核心，传统上是基于心电图(ECG)进行的。为了提高检测的鲁棒性和准确性，特别是在某些危急情况下，最近建议使用额外的生理信号，如动脉血压(BP)。在那里，心跳位置的估计需要从多个信号中融合信息。然而，据报道，在这一方向上所做的努力往往通过在单独获得的特定信号的中间估计数之间进行投票来间接获得多模态估计数。相反，我们建议直接融合多个信号中的信息而不需要中间估计，从而以稳健的方式估计心跳位置。方法：我们提出了一种从多个生理信号中学习融合特征的卷积神经网络(CNN)作为心跳检测器。该方法消除了手工挑选信号特定特征和ad hoc融合方案的需要。此外，由于数据驱动，同一算法从任意信号集中学习合适的特征。结果：使用PhysioNet 2014挑战数据库的ECG和BP信号，我们获得了94%的评分。此外，使用MIT-BIH心律失常数据库的两个心电通道，我们的得分为99.92%。这两个分数与以前报告的数据库特定结果相比都很好。此外，我们的检测器在各种临床条件下都达到了很高的准确度。结论：所提出的基于CNN的信息融合(CIF)算法具有可推广性、鲁棒性和有效性。意义：在医学信号监测系统中，即使只有一个信道子集是可靠的，我们的技术也能准确地估计心跳位置。
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英文标题：
《Robust Heartbeat Detection from Multimodal Data via CNN-based
  Generalizable Information Fusion》
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作者：
B S Chandra, C S Sastry and S Jana
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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：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Physics        物理学
二级分类：Medical Physics        医学物理学
分类描述：Radiation therapy. Radiation dosimetry. Biomedical imaging modelling.  Reconstruction, processing, and analysis. Biomedical system modelling and analysis. Health physics. New imaging or therapy modalities.
放射治疗。辐射剂量学。生物医学成像建模。重建、处理和分析。生物医学系统建模与分析。健康物理学。新的成像或治疗方式。
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英文摘要：
  Objective: Heartbeat detection remains central to cardiac disease diagnosis and management, and is traditionally performed based on electrocardiogram (ECG). To improve robustness and accuracy of detection, especially, in certain critical-care scenarios, the use of additional physiological signals such as arterial blood pressure (BP) has recently been suggested. There, estimation of heartbeat location requires information fusion from multiple signals. However, reported efforts in this direction often obtain multimodal estimates somewhat indirectly, by voting among separately obtained signal-specific intermediate estimates. In contrast, we propose to directly fuse information from multiple signals without requiring intermediate estimates, and thence estimate heartbeat location in a robust manner. Method: We propose as a heartbeat detector, a convolutional neural network (CNN) that learns fused features from multiple physiological signals. This method eliminates the need for hand-picked signal-specific features and ad hoc fusion schemes. Further, being data-driven, the same algorithm learns suitable features from arbitrary set of signals. Results: Using ECG and BP signals of PhysioNet 2014 Challenge database, we obtained a score of 94%. Further, using two ECG channels of MIT-BIH arrhythmia database, we scored 99.92\%. Both those scores compare favourably with previously reported database-specific results. Also, our detector achieved high accuracy in a variety of clinical conditions. Conclusion: The proposed CNN-based information fusion (CIF) algorithm is generalizable, robust and efficient in detecting heartbeat location from multiple signals. Significance: In medical signal monitoring systems, our technique would accurately estimate heartbeat locations even when only a subset of channels are reliable.
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PDF链接：
https://arxiv.org/pdf/1807.03232