Biomedical subjects

M S Woolfson

Publications and source records attributed to M S Woolfson.

3 recordsLinked to original sources

PubMed1992

Wavelet transform as a potential tool for ECG analysis and compression.

The recently introduced wavelet transform is a member of the class of time-frequency representations which include the Gabor short-time Fourier transform and Wigner-Ville distribution. Such techniques are of significance because of their ability to display the spectral content of a signal as time elapses. The value of the wavelet transform as a signal analysis tool has been demonstrated by its successful application to the study of turbulence and processing of speech and music. Since, in common with these subjects, both the time and frequency content of physiological signals are often of interest (the ECG being an obvious example), the wavelet transform represents a particularly relevant means of analysis. Following a brief introduction to the wavelet transform and its implementation, this paper describes a preliminary investigation into its application to the study of both ECG and heart rate variability data. In addition, the wavelet transform can be used to perform multiresolution signal decomposition. Since this process can be considered as a sub-band coding technique, it offers the opportunity for data compression, which can be implemented using efficient pyramidal algorithms. Results of the compression and reconstruction of ECG data are given which suggest that the wavelet transform is well suited to this task.

Electrocardiography↗

PubMed1991

Study of cardiac arrhythmia using the Kalman filter.

It has been known for some time that the variability of the R-R intervals in the electrocardiogram signal yields valuable information concerning the various types of arrhythmia that might be present. It has recently been suggested that the identification of cardiac arrhythmia might be possible by applying spectral analysis techniques to the data. An investigation is made into the possible application of the Kalman filter identifier in the calculation of time varying spectra of the data, with a view to studying the onset of arrhythmia and also short bursts of arrhythmia. To this end, data from the MIT-BIH database are analysed; in particular, cases of bigenimy, trigenimy, second degree block and ventricular flutter have been looked at. It is found that this technique can, in many cases, detect the onset of arrhythmia and sometimes actually identify the arrhythmia that is present. It is suggested that the Kalman filter identifier could have a general application in studying both the normal and arrhythmic segments of data to yield valuable medical information concerning the subject under study.

Arrhythmias, Cardiac↗

PubMed1990

Signal processing of the fetal electrocardiogram.

There has been considerable interest in recent years in the monitoring of the well-being of the fetus by the analysis of changes in morphology of the fetal electrocardiogram (FECG) signal. In this article, a brief review is first made of the use of the scalp electrode method to obtain an enhanced FECG complex. Subsequently, the problems in extracting the fetal signal from measurements taken from the abdomen of the mother are discussed. A comparison is made between two existing algorithms to extract the fetal signal: (1) the method of Akselrod; and (2) the adaptive filtering algorithm of Widrow. These algorithms are evaluated by considering the errors that could occur in locating the fetal R wave in the presence of noise.

Algorithms↗