Classification of vibroartographic signals based on wavelet transformation and machine learning techniques

2019;
: pp. 40-52
Authors:
1
Lviv Polytechnic National University, Department of Information-Measurement Technologies

Vibroartography is a method of medical diagnosis, designed for objective estimation of
human joint motor function in general and arthrokinematics of the knee joint in particular. The method is based on the analysis of signals of vibroacoustic emission. Vibroartography is
not so effective compared to methods such as radiography and magnetic resonance imaging
(MRI), but it is definitely a sensitive method for assessing the degree of knee joint dysfunction.
This paper presents the research results related to the design of a system for
vibroarthrographic signals computer processing. VAG signals are non-stationary, therefore,
for their analysis in this study, the discrete wavelet transform is used, which allows studying
not only the frequency components of the signal, but also their time localization. The novelty of
the proposed approach is based on the application of a discrete dyadic wavelet transform to
clear the biosignal from the impact of isolines drifts and random noise, as well as the use the
wavelet coefficients to form the diagnostically significant features. Scalogram analysis for 6
levels wavelet transforms allowed identifying a band from 78 to 780 Hz, where useful
diagnostic information is concentrated. Reconstruction of the signal in the specified band
resulted in the elimination of the destabilizing effects. After processing results of the wavelet
transform, twelve descriptors were chosen: standard deviation, mode and means of absolute
values for the four signal components. The results obtained for two classes scenario are the
following: accuracy of 94 %, sensitivity of 100% and specificity of 88%. For five classes
accuracy of 83%, sensitivity of 89% and specificity of 62% were achieved. Thus two classes
scenario demonstrated both high accuracy and sensitivity, while five classes scenario
demonstrated moderate results. The biggest overlap of descriptors is observed for the
neighboring classes. The main constraint in this study was a small number of signals – 26 in
each class. The duration of each recording is 6 seconds, at a sampling frequency of 10 kHz.
Records were separated into classes based on corresponding MRI images for each patient.

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