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Biomedical subjects

J S Barlow

Publications and source records attributed to J S Barlow.

At least 19 recordsLinked to original sources
PubMed1990

Changes in EEG mean frequency and spectral purity during spontaneous alpha blocking.

Spontaneously occurring brief periods of lower voltage irregular activity occurring amid a background of alpha activity (i.e., alpha blocking) in eyes-closed resting occipital EEG recordings from 32 healthy human subjects have been investigated to determine the extent of changes of mean frequency and of spectral purity (degree of regularity/irregularity of the EEG activity) during such periods. New methods for determining mean frequency and spectral purity (the latter as a new measure, the Spectral Purity Index, which has a maximum value of 1.0 for a pure sine wave) permit their conjoint evaluation over a 0.5 sec window that is advanced along the EEG in 0.1 sec steps, thus permitting almost continuous feature extraction. The findings indicate that, although spectral purity invariably decreased during the periods of lower voltage irregular activity, the mean frequency remained relatively unaltered, i.e., it remained unchanged or it increased or decreased slightly but at most by 2.5 Hz. These results suggest that, at least for the periods of lower voltage irregular activity occurring spontaneously amid an alpha background during eyes-closed occipital EEG recordings, it may be inaccurate (as some authors have already suggested) to use the term 'low-voltage fast (or beta) activity.'

Adult↗
PubMed1985

Computer characterization of tracé alternant and REM sleep patterns in the neonatal EEG by adaptive segmentation--an exploratory study.

The possible utility of the computer technique of adaptive segmentation in the comparative quantitative characterization of tracé alternant (TA) and REM sleep in the neonatal EEG has been explored in a pilot study of the EEGs of 3 full-term infants of ages 1, 13 and 23 days. The technique of adaptive segmentation, originally developed by its authors for automatically delimiting and characterizing different types of patterns within the same EEG recording, had previously been found effective for this purpose for normal and abnormal adult EEGs. The same computer program parameter values that had been found to be optimal for segmentation of adult EEGs were also found to be optimal for the neonatal ones, as typified by segmentation or demarcation of the bursts and interburst periods in tracé alternant. Adaptive segmentation, in conjunction with clustering of the resulting segments and computation of temporal profiles showing the times of occurrence of the different types of activity in a given phase of sleep together with the mean amplitude and mean frequency of each, supplemented by the respective power spectra, was found to be an effective way of characterizing these EEGs, including certain types of artifact. Further characterization was afforded by statistical summaries of segment durations.

Computers↗
PubMed1985

A general-purpose automatic multichannel electronic switch for EEG artifact elimination.

A general-purpose automatic multichannel electronic switch is described for eliminating artifact from EEG recordings. Although primarily intended for computer processing of EEG data, the device may on occasion be useful in clinical EEG (e.g., during electro-cautery). The switch is activated from a separate artifact-monitoring channel of the electroencephalograph (e.g., accelerometrically monitored head movement, electro-oculographically monitored eye movements or blinks), or from an EEG channel itself, if the artifact in the latter is very marked. Optionally, a sine wave can replace the EEG during the artifact period, and a separate output signal (e.g., for interrupting computer processing) is available for indicating the occurrence of artifact.

Computers↗
PubMed1985

Computerized EEG pattern classification by adaptive segmentation and probability density function classification. Clinical evaluation.

A series of 63 clinical EEGs showing a variety of normal and abnormal patterns was analysed by computer with particular reference to the different types of pattern within the same EEG. Boundaries between different patterns were established by means of adaptive segmentation, so that the duration of the resulting segments was determined by the particular EEG itself (thus the term 'adaptive'). Four channels from each EEG were analysed, paired (left and right) channels were simultaneously segmented and analysed interactively. Similar segments were then clustered without supervision by estimating a probability density function in a 2-dimensional 'feature space' having dimensions of mean frequency and mean power. Individual clusters emerged as well-defined peaks of the surface, individual segments or small groups of duration insufficient to constitute a separate cluster, being identified as 'singular events' (e.g., rare sharp waves, artifacts). The autocorrelation function was used to characterize the EEG both for the segmentation and for the subsequent clustering of the resulting segments. In confirmation of our previous work, adaptive segmentation based on the autocorrelation function of the EEG was found to be quite satisfactory. Unsupervised clustering by estimation of the probability density function in feature space was found to give the correct number of clusters (usually less than 5) in a majority of the records (65%), but in the remaining minority of cases (35%), either overclustering or underclustering occurred. Further, the 'singular events' were occasionally partly included in a formal cluster. Comparison of these results of EEG clustering by unsupervised probability density function estimation with earlier results obtained by supervised hierarchical clustering suggests that there may be subtle cues used by the electroencephalographer in the classification of EEG patterns which have not been adequately approximated by the computer algorithms thus far used in this work. Hence at least some minimal degree of supervision in the clustering process may be necessary, at least for the present. On the other hand, the method recommends itself for the representation of illustrative EEG summaries which, in conjunction with a short written report, would provide the clinical neurologist with a sufficient picture of the real EEG without, in most cases, the need to inspect the original record.

Action Potentials↗
PubMed1985

Moments of the power spectral density estimated from samples of the autocorrelation function (a robust procedure for monitoring changes in the statistical properties of lengthy non-stationary time series such as the EEG.

Accurate estimates of the statistical moments of the power spectral density (PSD) are obtained without computing the Fourier transform of the associated time series. An innovative analytical procedure is derived which reduces the problem to that of summing a small number of weighted samples of the autocorrelation function (ACF). This result significantly reduces the computational requirements for generating meaningful PSD shape descriptors and thus is especially important in biomedical applications where the cost and effort of monitoring lengthy non-stationary time series is a serious practical limitation. In addition the procedure is robust and therefore can be rigorously applied to any stochastic process to estimate its fundamental statistical properties.

Adult↗
PubMed1985

Methods of analysis of nonstationary EEGs, with emphasis on segmentation techniques: a comparative review.

Methods for analysis of nonstationary EEGs, that is, EEGs whose patterns undergo changes with time (e.g., alpha blocking, paroxysmal slow waves, onset of drowsiness/sleep, but excluding spikes/sharp waves) are reviewed. The concepts of stationarity and nonstationarity, and general techniques for their evaluation, are discussed. Simpler methods for monitoring for nonstationarity include running determinations of average amplitude and average period or interval. Piecewise stationary analysis includes characterization, by spectra obtained by fast Fourier transform or by autoregressive modeling, of sections of EEGs preselected to be stationary. In Kalman filtering, the autoregressive model itself becomes time-varying. Segmentation of the EEG into stationary lengths can be carried out on a fixed-interval basis (i.e., of successive, e.g., 1-s intervals), with clustering (grouping) or classification according to the features (e.g., spectra) of each interval, and concatenation of adjacent similar intervals. Alternatively, in adaptive (variable-interval) segmentation, the EEG is continuously monitored automatically for any significant departure from stationarity, and segment boundaries are placed accordingly. A number of applications of the various methods are included, with examples of succinct summary displays. Problems and prospects are discussed.

Algorithms↗
PubMed1975

A 16-channel cassette tape recorder system for clinical EEGs.

A 16-channel EEG tape recorder system having a frequency response of DC-100 Hz for each channel is described. The system utilized standard commercially available highfidelity audio tape decks in conjunction with specially designed circuits for time-division multiplexing a balanced amplitude modulation

Electroencephalography↗