The Application of Classification Techniques to Biomedical Data, with Particular Reference to Ultrasonic Doppler Blood Velocity Waveforms

The automatic classification of waveforms, derived from the body, into normal and abnormal, or into those with different degrees of abnormality, is becoming more feasible as microcomputer systems become readily available. There are many ways to achieve such classifications, but the best methods in any instance will depend on many factors. In this study, several methods have been used to classify ultrasonic Doppler blood velocity waveforms, derived from humans with peripheral arterial disease and dogs with stenoses of different severities implanted into their hind limbs. The results have been compared, and the advantages and disadvantages of the methods are discussed.

[1]  R. N. Macsween,et al.  Ultrasonic characterisation of diffuse liver disease - the relative importance of frequency content in the A-scan signal. , 1982, Ultrasound in medicine & biology.

[2]  T. R. P. Martin,et al.  Objective feature extraction applied to the diagnosis of carotid artery disease using a Doppler ultrasound technique , 1980 .

[3]  D H Evans,et al.  On-line classification of arterial stenosis severity using principal component analysis applied to Doppler ultrasound signals. , 1982, Clinical physics and physiological measurement : an official journal of the Hospital Physicists' Association, Deutsche Gesellschaft fur Medizinische Physik and the European Federation of Organisations for Medical Physics.

[4]  J P Woodcock,et al.  Physiological interpretation of Doppler-shift waveforms--III. Clinical results. , 1980, Ultrasound in medicine & biology.

[5]  D. E. Raeside,et al.  Fourier analysis of the echocardiogram. , 1978, Physics in medicine and biology.

[6]  D H Evans,et al.  The effect of proximal stenosis on Doppler waveforms: a comparison of three methods of waveform analysis in an animal model. , 1981, Clinical physics and physiological measurement : an official journal of the Hospital Physicists' Association, Deutsche Gesellschaft fur Medizinische Physik and the European Federation of Organisations for Medical Physics.

[7]  D. H. King,et al.  The Quantitative Analysis of Occlusive Peripheral Arterial Disease By a Non-Intrusive Ultrasonic Technique , 1971, Angiology.

[8]  F. M. Greene,et al.  Computer based pattern recognition of carotid arterial disease using pulsed Doppler ultrasound. , 1982, Ultrasound in medicine & biology.

[9]  Brain Tissue Classification by Its Ultrasonic Backscatter , 1981, IEEE Transactions on Sonics and Ultrasonics.