摘要翻译：
地震心动图(SCG)是一种可用于心脏活动监测的非侵入性方法。本文提出了一种新的心电图(ECG)无关的方法，用于利用SCG信号估计低肺容量和高肺容量（分别为LLV和HLV）时相的心率(HR)。本研究对7例健康受试者同时测量SCG、ECG和呼吸流速(RFR)信号。根据RFR计算肺容量信息，并将SCG事件分为低肺容量组和高肺容量组。然后用LLV和HLV SCG事件估计受试者的心率，以及LLV和HLV在3种不同体位（仰卧、45度仰卧和坐位）下的心率。通过对标准心电测量结果的测试，验证了该算法的性能。结果表明，由SCG和ECG信号估计的HR值符合得很好（偏差为0.08bpm)。所有受试者在HLV(HR$_\text{HLV}$)期间的HR均高于LLV(HR$_\text{LLV}$)。仰卧位、45度第一次试验、45度第二次试验和坐位的HR$_\text{HLV}$/HR$_\text{LLV}$比值分别为1.11$\pm$0.07、1.08$\pm$0.05、1.09$\pm$0.04和1.09$\pm$0.04（平均$\pm$SD)。这种心率变异性可能是由于，至少部分是由于众所周知的呼吸窦性心律失常。从SCG信号监测HR可用于不同的临床应用，包括可穿戴式心脏监测系统。
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英文标题：
《Heart Rate Monitoring During Different Lung Volume Phases Using
  Seismocardiography》
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作者：
Amirtaha Taebi, Andrew J Bomar, Richard H Sandler, Hansen A Mansy
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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：
  Seismocardiography (SCG) is a non-invasive method that can be used for cardiac activity monitoring. This paper presents a new electrocardiogram (ECG) independent approach for estimating heart rate (HR) during low and high lung volume (LLV and HLV, respectively) phases using SCG signals. In this study, SCG, ECG, and respiratory flow rate (RFR) signals were measured simultaneously in 7 healthy subjects. The lung volume information was calculated from the RFR and was used to group the SCG events into low and high lung-volume groups. LLV and HLV SCG events were then used to estimate the subjects HR as well as the HR during LLV and HLV in 3 different postural positions, namely supine, 45 degree heads-up, and sitting. The performance of the proposed algorithm was tested against the standard ECG measurements. Results showed that the HR estimations from the SCG and ECG signals were in a good agreement (bias of 0.08 bpm). All subjects were found to have a higher HR during HLV (HR$_\text{HLV}$) compared to LLV (HR$_\text{LLV}$) at all postural positions. The HR$_\text{HLV}$/HR$_\text{LLV}$ ratio was 1.11$\pm$0.07, 1.08$\pm$0.05, 1.09$\pm$0.04, and 1.09$\pm$0.04 (mean$\pm$SD) for supine, 45 degree-first trial, 45 degree-second trial, and sitting positions, respectively. This heart rate variability may be due, at least in part, to the well-known respiratory sinus arrhythmia. HR monitoring from SCG signals might be used in different clinical applications including wearable cardiac monitoring systems.
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PDF链接：
https://arxiv.org/pdf/1803.10346