Abstract
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.
Highlights
Cardiovascular disease is a leading cause of death in the United States, accounting for about 24% of total deaths in 2010 [1]
The objective of this study was to compare the ability of six different approaches in providing accurate time-frequency distribution (TFD) estimates for seismocardiographic (SCG) signals
The errors in estimating instantaneous frequency (IF) were lowest for polynomial chirplet transform (PCT) followed by short-time Fourier transform (STFT) for most test signals
Summary
Cardiovascular disease is a leading cause of death in the United States, accounting for about 24% of total deaths in 2010 [1]. Auscultation of heart sounds has been providing useful diagnostic information and is a common test performed during physical examinations. Computer analysis of these sounds can provide additional quantitative diagnostic information that may be helpful for screening patients suspected of heart disease. Measurement of cardiac vibrations was performed as early as the start of the 20th century [7]. Many variations within this approach, such as vibrocardiography (VCG), kinetocardiography (KCG), ballistocardiography (BCG), cardiokymography (CKG), and apexcardiography have been described [8,9,10,11,12,13,14,15,16,17,18]
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