Time-Frequency Distribution of Seismocardiographic Signals: A Comparative Study.
Time-Frequency Distribution of Seismocardiographic Signals: A Comparative Study.
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DOI：
10.3390/bioengineering4020032
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发表时间：
2017-04-07
期刊：
影响因子：
--
通讯作者：
Mansy HA
中科院分区：
文献类型：
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作者：
Taebi A;Mansy HA
地震心动图(SCG)信号特征的准确估计有助于成功地对健康和疾病中的信号进行表征和分类。这可能导致诊断和监测心脏功能的新方法。时频分布(TFD)通常被用来估计谱时间信号的特征。在这项研究中，不同的TFD(如短时傅立叶变换(STFT)、多项式调频小波变换(PCT)和具有不同母函数的连续小波变换(CWT))的性能得到了评估，并被用于分析实际的SCG。根据TFD确定瞬时频率(IF)，并计算模拟信号的IF估计误差。结果表明，中频误差最小取决于TFD和测试信号。对于大多数测试信号，短时傅里叶变换的误差小于连续小波变换方法。对于模拟的SCG，Morlet CWT比其他CWTS更准确地估计IF，但Morlet没有提供比STFT或PCT明显的优势。PCT的IF估计具有最一致的准确性，并且似乎更适合于估计实际SCG信号的IF。PCT分析显示，8名健康受试者的SCG在9.20±0.48、25.84±0.77、50.71±1.83赫兹(均值±扫描电子显微镜)有多个谱峰。这些特征可能被证明是SCG表征和分类的有用特征。
Accurate estimation of seismocardiographic (SCG) signal features can help successful signal characterization and classification in health and disease. This may lead to new methods for diagnosing and monitoring heart function. Time-frequency distributions (TFD) were often used to estimate the spectrotemporal signal features. In this study, the performance of different TFDs (e.g., short-time Fourier transform (STFT), polynomial chirplet transform (PCT), and continuous wavelet transform (CWT) with different mother functions) was assessed using simulated signals, and then utilized to analyze actual SCGs. The instantaneous frequency (IF) was determined from TFD and the error in estimating IF was calculated for simulated signals. Results suggested that the lowest IF error depended on the TFD and the test signal. STFT had lower error than CWT methods for most test signals. For a simulated SCG, Morlet CWT more accurately estimated IF than other CWTs, but Morlet did not provide noticeable advantages over STFT or PCT. PCT had the most consistently accurate IF estimations and appeared more suited for estimating IF of actual SCG signals. PCT analysis showed that actual SCGs from eight healthy subjects had multiple spectral peaks at 9.20 ± 0.48, 25.84 ± 0.77, 50.71 ± 1.83 Hz (mean ± SEM). These may prove useful features for SCG characterization and classification.