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Application of the continuous wavelet transform for the analysis of pathological severity degree of electromyograms (EMGs) signals

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Mokdad A. , Debbal S. M. E. A. , Meziani F.

Polish Journal of Medical Physics and Engineering

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2020

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tom Vol. 26, Iss. 3

149--154

EN

The aim of this work was twofold: first, to propose signal processing methods for assessing the temporal and spectral changes of parameters (mean absolute value, the energy and standard deviation as temporal parameters, total and mean power as frequency parameters) of the surface myoelectric signal of the various patient groups like normal, myopathic and neuropathic during muscles contraction of biceps. Secondly, to analyze this electrical manifestation of neuromuscular disorders by the implementation of time-frequency analysis using continuous wavelet that allows us to qualify this method to evaluate, appreciate the pathology and determine its degree of severity which was unable by extracting mentioned parameters. Our results showed that this approach presents satisfactory performances especially to follow patients with the least severe pathology.

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Preliminary study in the analysis of the severity of cardiac pathologies using the higher-order spectra on the heart-beats signals

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Berraih S. A. , Baakek Y. N. E. H. , Debbal S. M. E. A.

Polish Journal of Medical Physics and Engineering

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2021

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tom Vol. 27, Iss. 1

73--85

EN

Phonocardiography is a technique for recording and interpreting the mechanical activity of the heart. The recordings generated by such a technique are called phonocardiograms (PCG). The PCG signals are acoustic waves revealing a wealth of clinical information about cardiac health. They enable doctors to better understand heart sounds when presented visually. Hence, multiple approaches have been proposed to analyze heart sounds based on PCG recordings. Due to the complexity and the high nonlinear nature of these signals, a computer-aided technique based on higher-order statistics (HOS) is employed, it is known to be an important tool since it takes into account the non-linearity of the PCG signals. This method also known as the bispectrum technique, can provide significant information to enhance the diagnosis for an accurate and objective interpretation of heart condition. The objective expected by this paper is to test in a preliminary way the parameters which can make it possible to establish a discrimination between the various signals of different pathologies and to characterize the cardiac abnormalities. This preliminary study will be done on a reduced sample (nine signals) before applying it subsequently to a larger sample. This work examines the effectiveness of using the bispectrum technique in the analysis of the pathological severity of different PCG signals. The presented approach showed that HOS technique has a good potential for pathological discrimination of various PCG signals.

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Study of the impact of clicks and murmurs on cardiac sounds S1 and S2 through bispectral analysis

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Baakek Y. N. E. H. , Debbal I. , Hidayat B. , Debbal S. M. E. A.

Polish Journal of Medical Physics and Engineering

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2021

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tom Vol. 27, Iss. 1

63--72

EN

This paper presents a study of the impact of clicks, and murmurs on cardiac sound S1, and S2, and the measure of severity degree through synchronization degree between frequencies, using bispectral analysis. The algorithm is applied on three groups of Phonocardiogram (PCG) signal: group A represents PCG signals having a morphology similar to that of the normal PCG signal without click or murmur, group B represents PCG signals with a click (reduced murmur), and group C represent PCG signals with murmurs. The proposed algorithm permits us to evaluate and quantify the relationship between the two sounds S1 and S2 on one hand and between the two sounds, click and murmur on the other hand. The obtained results show that the clicks and murmurs can affect both the heart sounds, and vice versa. This study shows that the heart works in perfect harmony and that the frequencies of sounds S1, S2, clicks, and murmurs are not accidentally generated; but they are generated by the same generator system. It might also suggest that one of the obtained frequencies causes the others. The proposed algorithm permits us also to determine the synchronization degree. It shows high values in group C; indicating high severity degrees, low values for group B, and zero in group A. The algorithm is compared to Short-Time Fourier Transform (STFT) and continuous wavelet transform (CWT) analysis. Although the STFT can provide correctly the time, it can’t distinguish between the internal components of sounds S1 and S2, which are successfully determined by CWT, which, in turn, cannot find the relationship between them. The algorithm was also evaluated and compared to the energetic ratio. the obtained results show very satisfactory results and very good discrimination between the three groups. We can conclude that the three algorithms (STFT, CWT, and bispectral analysis) are complementary to facilitate a good approach and to better understand the cardiac sounds.

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Diagnosis of amyotrophic lateral sclerosis (ALS) disorders based on electromyogram (EMG) signal analysis and feature selection

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Mokdad A. , Debbal S. M. E. A. , Meziani F.

Polish Journal of Medical Physics and Engineering

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2020

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tom Vol. 26, Iss. 3

155--160

EN

Electromyogram signal (EMG) provides an important source of information for the diagnosis of neuromuscular disorders. In this study, we proposed two methods of analysis which concern the bispectrum and continuous wavelet transform (CWT) of the EMG signal then a comparison is made to select which one is the most suitable to identify an abnormality in biceps brachii muscle in the main purpose is to assess the pathological severity in bifrequency and time-frequency analysis applying respectively bispectrum and CWT. Then four time and frequency features are extracted and three popular machine learning algorithms are implemented to differentiate neuropathy and healthy conditions of the selected muscle. The performance of these time and frequency features are compared using support vector machine (SVM), linear discriminate analysis (LDA) and K-Nearest Neighbor (KNN) classifier performance. The results obtained showed that the SVM classifier yielded the best performance with an accuracy of 95.8%, precision of 92.59% and specificity of 92%. followed by respectively KNN and LDA classifier that achieved respectively an accuracy of 92% and 91.5%, precision of 92% and 85.4%, and specificity of 92% and 83%.