Biomedical subjects

M A Shariati

Publications and source records attributed to M A Shariati.

2 recordsLinked to original sources

PubMed1993

A comparison of colour flow imaging algorithms.

In this paper we will present and explain coherently the two main algorithms which are currently used in commercial colour flow imaging systems, namely the frequency domain based autocorrelation algorithm and the time domain based cross correlation algorithm. The autocorrelation algorithm is a robust optimum mean frequency baseband estimator, for both high and low signal to noise ratio (SNR). Its main disadvantage is its aliasing problem. However the cross correlation algorithm does not suffer from this problem, and this has been one of the main reasons for its development. The mathematics behind these two algorithms and the general data processing steps used for their implementation will be also presented. The subject of clutter removal filtering will be discussed and valuable points will be made. A practical relevant approach on how to discriminate between wanted blood flow related estimates and unwanted false estimates due to noise will be presented. On the aliasing problem of the autocorrelation algorithm, a promising solution known as spatial tracking of estimated mean frequency does exist, which will be also introduced in detail. Towards the end of this paper other algorithms, which are currently the subject of research and have potential for development, will be discussed.

Algorithms↗

PubMed1993

Linear time series analysis of long term fetal heart rate variability patterns.

Long term fetal heart rate variability (LFHRV) is equivalent to detrended (when the time series trend removed) indirectly derived averaged fetal heart rate (FHR) time series. Up to now LFHRV is looked for visually in FHR traces, by obstetricians and midwifes for determination of fetal condition. The detrended averaged FHR data or LFHRV data is a random correlated non-stationary (in second moment) time series. In this paper we have applied linear stochastic time series analysis technique to identify a parsimonious stochastic model, for parametric numerical representation of the random cyclical patterns observed in short 2 minutes quasi-stationary contiguous blocks of LFHRV data. The parametric estimation technique used is based upon the optimum exact Maximum likelihood estimation (which uses Kalman filtering as part of its implementation). Diagnostics performed on the residuals indicated that a second order autoregressive model is a statistically adequate model in capturing variability patterns observed in 2 minute data windows of detrended average FHR. Also through further analysis of the spectral behavior of this identified model, pseudo-periodicity (or random periodicity) which for a long time was visually detected, can now be detected via this numerical procedure.

Female↗