The denoising of electrocardiogram (ECG) represents the entry point for the processing of this signal. The widely algorithms for ECG denoising are based on discrete wavelet transform (DWT). In the other side the performances of denoising process considerably influence the operations that follow. These performances are quantified by some ratios such as the output signal on noise (SNR) and the mean square error (MSE) ratio. This is why the optimal selection of denoising parameters is strongly recommended. The aim of this work is to define the optimal wavelet function to use in DWT decomposition for a specific case of ECG denoising. The choice of the appropriate threshold method giving the best performances is also presented in this work. Finally the criterion of selection of levels in which the DWT decomposition must be performed is carried on this paper. This study is applied on the electromyography (EMG), baseline drift and power line interference (PLI) noises.

This paper shows that time and frequency domains may be used to evaluate force of skeletal muscle. The surface electromyography sEMG can be used to quantify force level of the muscle contraction. The main objective of this work is the study of the relationship between different features of sEMG signal and force level of contraction. The most popular time and frequency parameters extracted from sEMG signal are root mean square (RMS), mean absolute value (MAV), median frequency (MDF) and mean power frequency (MPF). The results of the experiment showed an increasing in time domain parameters values and a decreasing in frequency domain parameters values during four successive contractions significantly with the increasing in force level contraction. Furthermore, the sensitivity is calculated for each parameters. (RMS) and (MPF) have been selected as the best features to evaluate muscle force.

This paper deals with the characterization of the fetal electrocardiogram signal by using only the continuous wavelet transform. Clinically significant information in the fetal electrocardiogram signal is often masked by maternal electrocardiogram mECG considered as the most predominant interference. The aim of this work is to characterize the complex QRS and to estimate the RR interval in order to diagnose fetal heart arrhythmia such as Bradycardia and Tachycardia and this without being obliged to extract the fetal signal from that of the mother. We focus on both time domain and frequency domain to extract the useful characteristics of the signal.

Electromyography (EMG) is a very suitable technique for the diagnosis of the skeletal muscle state. This work introduces a powerful tool for the analysis and the classification of myopathy and normal EMG signals. Continuous wavelet transform (CWT) was applied to a total of 406 EMG records. For the classification purpose, four statistical features were extracted from the continuous wavelet analysis. The classification is performed using k Nearest Neighborhood (k-NN) and Support Vector Machine (SVM) classifiers. 10-fold cross-validation method was used in this study since it delivered higher results. To evaluate the performances of the classifiers, metrics such as specificity, sensitivity and accuracy were calculated. Experimental results showed that both SVM and k-NN classifiers yielded high results. An accuracy value of $91.11 \pm 0.38$ (Mean ± Standard Deviation) was obtained with k-NN classifier using 9 nearest neighbors. In regards to SVM, the classifier achieved an accuracy of $91.01\pm 0.28$ using the Radial Basis Function (RBF) kernel. The proposed technique demonstrated that it is an efficient tool in the discrimination between myopathic patients and normal subjects.

In this work a novel automated method is presented for the detection and characterization of the QRS complexes of the fetus cardiac activity by using single channel maternal ECG recordings. The proposed algorithm is based on the continuous wavelet transform CWT and non-negative matrix factorization NMF. Clinically significant information in the fetal electrocardiogram signal is often masked by maternal electrocardiogram mECG considered as the most predominant interference. The aim of this work is to characterize the complex without being obliged to extract the fetal signal from that of the mother. We focus on both time domain and frequency domain to extract the useful characteristics of the signal.