Amirtaha Taebi

Effect of Noise on Time-frequency Analysis of Vibrocardiographic Signals

Recordings of biological signals such as vibrocardiography often contain contaminating noise. Noise sources may include respiratory, gastrointestinal, and muscles movement, or environmental noise. Depending on individual physiology and sensor location, the vibrocardiographic (VCG) signals may be obscured by these noises in the time-frequency plane, which may interfere with automated characterization of VCG. In this study, polynomial chirplet transform (PCT) and smoothed pseudo Wigner-Ville distribution (SPWVD) were used to estimate the instantaneous frequency (IF) of two simulated VCG signals. One simulated signal contained a time-varying IF while the other had a fixed IF. The error in estimating IF was then calculated for signal-to-noise ratios (SNR) from −10 to 10 dB. Analysis was repeated 100 times at each level of noise using randomized sets of white noise. Error analysis showed that the range of errors in estimating IF was wider when SNR decreased. Results also showed that PCT tended to outperform SPWVD at high SNR. For example, PCT was more accurate at SNR > 3 dB for a simulated VCG signal with constant frequency components, at SNR>−10 dB for a simulated VCG signal with time-varying frequency, and at SNR > 0 for an actual VCG.

Time-Frequency Distribution of Seismocardiographic Signals: A Comparative Study

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.

Mechanical energy assessment of adult with Down syndrome during walking with obstacle avoidance

The aim of this study is analyzing the differences between plane walking and stepping over an obstacle for two groups of healthy people and people with Down syndrome and then, evaluating the movement efficiency between the groups by comprising of their mechanical energy exchanges. 39 adults including two groups of 21 people with Down syndrome (age: 21.6 ± 7 years) and 18 healthy people (age: 25.1 ± 2.4 years) participated in this research. The test has been done in two conditions, first in plane walking and second in walking with an obstacle (10% of the subjects height). The gait data were acquired using quantitative movement analysis, composed of an optoelectronic system (Elite2002, BTS) with eight infrared cameras. Mechanical energy exchanges are computed by dedicated software and finally the data including spatiotemporal parameters, mechanical energy parameters and energy recovery of gait cycle are analyzed by statistical software to find significant differences. Regards to spatiotemporal parameters velocity and step length are lower in people with Down syndrome. Mechanical energy parameters particularly energy recovery does not change from healthy people to people with Down syndrome. However, there are some differences in inter-group through plane walking to obstacle avoidance and it means people with Down syndrome probably use their residual abilities in the most efficient way to achieve the main goal of an efficient energy recovery.

Pressure loss and sound generated in a miniature pig airway tree model

Background: Pulmonary auscultation is a common tool for diagnosing various respiratory diseases. Previous studies have documented many details of pulmonary sounds in humans. However, information on sound generation and pressure loss inside animal airways is scarce. Since the morphology of animal airways can be significantly different from human, the characteristics of pulmonary sounds and pressure loss inside animal airways can be different. Objective: The objective of this study is to investigate the sound and static pressure loss measured at the trachea of a miniature pig airway tree model based on the geometric details extracted from physical measurements. Methods: In the current study, static pressure loss and sound generation measured in the trachea was documented at different flow rates of a miniature pig airway tree. Results: Results showed that the static pressure and the amplitude of the recorded sound at the trachea increased as the flow rate increased. The dominant frequency was found to be around 1840-1870 Hz for flow rates of 0.2-0.55 lit/s. Conclusion: The results suggested that the dominant frequency of the measured sounds remained similar for flow rates from 0.20 to 0.55 lit/s. Further investigation is needed to study sound generation under different inlet flow and pulsatile flow conditions.