摘要翻译：
时域算法的重点是利用移动窗口检测局部极大值或局部极小值，从而找到BCG信号主要J峰之间的间隔。然而，由于卡介苗信号的非线性和非平稳性，这种方法有许多局限性。这是因为卡介苗信号不显示一致的J峰，这通常是通宵在家监测的情况，特别是对虚弱的老年人。此外，运动伪影无疑会影响其精度。第二，频域算法不提供关于间隔的信息。尽管如此，它们可以提供有关心率变异性的信息。这通常是通过对估计谱的对数进行快速傅立叶变换或逆傅立叶变换来完成的，即使用滑动窗口的信号倒谱。此后，在特定频率范围内获得主频。这些算法的局限性是频谱中的峰值可能会变宽，并可能出现多个峰值，这可能会给生命体征的测量带来问题。最后，小波域算法的目标是将信号分解成不同的分量，从而选择与生命体征一致的分量，即只包含心脏周期或呼吸周期的信息。经验模态分解是小波分解的一种替代方法，也是处理心肺信号等非线性、非平稳信号的一种非常合适的方法。除了上述算法之外，机器学习方法已经被用于测量心跳。然而，人工标记训练数据是一个限制性质。
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英文标题：
《Ballistocardiogram Signal Processing: A Literature Review》
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作者：
Ibrahim Sadek
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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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英文摘要：
  Time-domain algorithms are focused on detecting local maxima or local minima using a moving window, and therefore finding the interval between the dominant J-peaks of ballistocardiogram (BCG) signal. However, this approach has many limitations due to the nonlinear and nonstationary behavior of the BCG signal. This is because the BCG signal does not display consistent J-peaks, which can usually be the case for overnight, in-home monitoring, particularly with frail elderly. Additionally, its accuracy will be undoubtedly affected by motion artifacts. Second, frequency-domain algorithms do not provide information about interbeat intervals. Nevertheless, they can provide information about heart rate variability. This is usually done by taking the fast Fourier transform or the inverse Fourier transform of the logarithm of the estimated spectrum, i.e., cepstrum of the signal using a sliding window. Thereafter, the dominant frequency is obtained in a particular frequency range. The limit of these algorithms is that the peak in the spectrum may get wider and multiple peaks may appear, which might cause a problem in measuring the vital signs. At last, the objective of wavelet-domain algorithms is to decompose the signal into different components, hence the component which shows an agreement with the vital signs can be selected i.e., the selected component contains only information about the heart cycles or respiratory cycles, respectively. An empirical mode decomposition is an alternative approach to wavelet decomposition, and it is also a very suitable approach to cope with nonlinear and nonstationary signals such as cardiorespiratory signals. Apart from the above-mentioned algorithms, machine learning approaches have been implemented for measuring heartbeats. However, manual labeling of training data is a restricting property.
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PDF链接：
https://arxiv.org/pdf/1807.00951