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Biomedical subjects

B M Sayers

Publications and source records attributed to B M Sayers.

46 records · Page 3Linked to original sources
PubMedDate not supplied

Fully objective ERA by phase spectral analysis.

A method of ERA using the Vertex potential is presented which is entirely objective. The result is given by means of a statistical evaluation which is derived automatically. The method is based on pattern analysis of the V-potential, the technique relying essentially on analysis of the phase spectrum of the appropriate Fourier components.

Acoustic Stimulation↗
PubMedDate not supplied

Pattern analysis of auditory-evoked EEG potentials.

A pattern analysis approach to the detection of auditory evoked potentials in the EEG is reported. Pattern aspects of individual post-stimulus responses have been examined first through their correlation coefficients using the suprathreshold average evoked potential as a reference pattern, and second by the ensemble distribution of phase values of Fourier spectral components, employed to characterise signal pattern. The correlation study indicated that suprathreshold stimuli act to increase the reference pattern similarity of individual records. Individual harmonic phase values are distributed approximately uniformly for unstimulated EEG, but were demonstrated to be increasingly aggregated for increasingly suprathreshold stimuli. It is proposed that a fully objective audiometric technique can be based on a phase-spectral technique.

Acoustic Stimulation↗
PubMedDate not supplied

Statistical variability of biomedical data: part 2. The influence of serial correlations on power estimates, and on comparative testing of samples.

The effect of serial-correlation structure of biomedical data on the statistical-sampling variability of power estimates is investigated, extending a previous study of mean value estimates. The degrees of freedom content for power DFp in a k-point sample is shown to be dependent on the serial-correlation structure in a generally different way from the DFk, the corresponding measure for the mean. The sampling distribution of power is also investigated. The influence of serial correlation structure on the behaviour of traditional small-sample statistical tests is also of interest; in the case of the t-test the effects can be severe but can be compensated, leading to a new variable, ct, which for correlated data, behaves rather like t for uncorrelated data. The approach is applied to the case of long-term records of systolic blood-pressure data.

Blood Pressure↗
PubMedDate not supplied

Statistical variability of biomedical data: Part 1. The influence of serial correlation on mean value estimates.

Many biomedical variables are subject to large short-term fluctuations; so, measurements of the variable requires statistical improvement. Such variables also commonly exhibit longitudinal correlation due to systematic variations contributed by underlying biological mechanisms and sometimes constituting most of the variability. This correlation structure interferes with the operation of statistical procedures, such as longitudinal averaging to improve resolution of the mean value, and manipulations designed to provide objective comparative assessments of data samples. A convenient approach to evaluating and overcoming these problems can be made through the degrees of freedom concept. It is shown how the degrees of freedom content of the sample of serially correlated data is related to the correlation structure of the variable and how this affects variability of the mean in different sized samples. The approach is confirmed both by simulation and by empirical studies on several biomedical variables: various intra-arterial and left ventricular blood-pressure measurements, heart rate, and nystagmus inter-beat intervals in the stimulated electro-oculogram. The implications for measurement are discussed, specifically in respect of sampling protocols to improve resolution to a specified extent (for which a microprocessor instrument has been designed), as well as in determining the inherent longitudinal resolution of a data sequence and the effective precision displayed by the observations.

Blood Pressure Determination↗