用多项式Chirplet分析地震心电信号 变换与平滑伪Wigner-Ville分布

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摘要翻译：
心震(SCG)信号是由心脏活动引起的胸部表面振动。这些信号可为诊断和监测心功能提供一种方法。健康和疾病中SCG信号的成功分类依赖于准确的信号表征和特征提取。确定信号特征的一种方法是估计信号的时频特性。为此，采用了短时傅立叶变换(STFT)、多项式chirplet变换(PCT)、Wigner-Ville分布(WVD)和平滑伪Wigner-Ville分布(SPWVD)四种不同的时频分布(TFD)方法。生成已知时频特性的合成SCG信号，并用于评估不同TFDs提取SCG谱特征的准确性。利用不同的TFD，确定了各合成信号的瞬时频率(IF)，并计算了估计瞬时频率的误差(NRMSE)。对于合成信号，STFT比WVD具有更低的NRMSE。然而，PCT和SPWVD是更准确的估计，特别是对时变频率的信号。PCT和SPWVD也提供了更好的信号频率成分之间的区分。因此，本研究结果提示PCT和SPWVD是判断SCG信号IF更为可靠的方法。对实际SCG信号的分析表明，这些信号具有多个频谱成分，频率略有时变。需要更多的研究来研究健康受试者以及不同心脏状况患者的SCG光谱特性。
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英文标题：
《Analysis of Seismocardiographic Signals Using Polynomial Chirplet
  Transform and Smoothed Pseudo Wigner-Ville Distribution》
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作者：
Amirtaha Taebi, Hansen A Mansy
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最新提交年份：
2017
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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：
  Seismocardiographic (SCG) signals are chest surface vibrations induced by cardiac activity. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction. One approach of determining signal features is to estimate its time-frequency characteristics. In this regard, four different time-frequency distribution (TFD) approaches were used including short-time Fourier transform (STFT), polynomial chirplet transform (PCT), Wigner-Ville distribution (WVD), and smoothed pseudo Wigner-Ville distribution (SPWVD). Synthetic SCG signals with known time-frequency properties were generated and used to evaluate the accuracy of the different TFDs in extracting SCG spectral characteristics. Using different TFDs, the instantaneous frequency (IF) of each synthetic signal was determined and the error (NRMSE) in estimating IF was calculated. STFT had lower NRMSE than WVD for synthetic signals considered. PCT and SPWVD were, however, more accurate IF estimators especially for the signal with time-varying frequencies. PCT and SPWVD also provided better discrimination between signal frequency components. Therefore, the results of this study suggest that PCT and SPWVD would be more reliable methods for estimating IF of SCG signals. Analysis of actual SCG signals showed that these signals had multiple spectral components with slightly time-varying frequencies. More studies are needed to investigate SCG spectral properties for healthy subjects as well as patients with different cardiac conditions.
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PDF链接：
https://arxiv.org/pdf/1711.11138

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关键词：Wigner 电信号 PLE let IRP different Ville 方法 spectral NRMSE