This paper introduces an automatic feature extraction system for gear and bearing fault diagnosis using wavelet-based signal processing. Vibration signals recorded from two experimental set-ups were processed for gears and bearing conditions. Four statistical features were selected: standard deviation, variance, kurtosis, and fourth central moment of continuous wavelet coefficients of synchronized vibration signals (CWC-SVS). In this research, the mother wavelet selection is broadly discussed. 324 mother wavelet candidates were studied, and results show that Daubechies 44 (db44) has the most similar shape across both gear and bearing vibration signals. Next, an automatic feature extraction algorithm is introduced for gear and bearing defects. It also shows that the fourth central moment of CWC-SVS is a proper feature for both bearing and gear failure diagnosis. Standard deviation and variance of CWC-SVS demonstrated more appropriate outcome for bearings than gears. Kurtosis of CWC-SVS illustrated the acceptable performance for gears only. Results also show that although db44 is the most similar mother wavelet function across the vibration signals, it is not the proper function for all wavelet-based processing.

Application of mother wavelet functions for automatic gear and bearing fault diagnosis

Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization

We propose in this paper the use of Wavelet transform (WT) to detect human falls using a ceiling mounted Doppler range control radar. The radar senses any motions from falls as well as nonfalls due to the Doppler effect. The WT is very effective in distinguishing the falls from other activities, making it a promising technique for radar fall detection in nonobtrusive inhome elder care applications. The proposed radar fall detector consists of two stages. The prescreen stage uses the coefficients of wavelet decomposition at a given scale to identify the time locations in which fall activities may have occurred. The classification stage extracts the time-frequency content from the wavelet coefficients at many scales to form a feature vector for fall versus nonfall classification. The selection of different wavelet functions is examined to achieve better performance. Experimental results using the data from the laboratory and real inhome environments validate the promising and robust performance of the proposed detector.

Doppler Radar Fall Activity Detection Using the Wavelet Transform

Myocardial infarction (MI), also known as a heart attack, is a leading cause of mortality in the world. Spatial vectorcardiogram (VCG) signals are recorded on the body surface to monitor the underlying cardiac electrical activities in three orthogonal directions of the body, namely, frontal, transverse, and sagittal planes. The 3-D VCG vector loops provide a new way to study the cardiac dynamical behaviors, as opposed to the conventional time-delay reconstructed phase space from a single ECG trace. However, few, if any, previous approaches studied the relationships between cardiac disorders and recurrence patterns in VCG signals. This paper presents the recurrence quantification analysis (RQA) of VCG signals in multiple wavelet scales for the identification of cardiac disorders. The linear classification models using multiscale RQA features were shown to detect MI with an average sensitivity of 96.5% and an average specificity of 75% in the randomized classification experiments of PhysioNet Physikalisch-Technische Bundesanstalt database, which is comparable to the performance of human experts. This study is strongly indicative of potential automated MI classification algorithms for diagnostic and therapeutic purposes.

Multiscale Recurrence Quantification Analysis of Spatial Cardiac Vectorcardiogram Signals

Research highlights? Daubechies 44 wavelet basis is the most similar function across various biosignals. ? High order Daubechies functions are useful to extract features from 1-D biosignals. ? For wavelet signal processing, selection of mother function similar to signal is not always a proper strategy. During the last two decades, wavelet transform has become a common signal processing technique in various areas. Selection of the most similar mother wavelet function has been a challenge for the application of wavelet transform in signal processing. This paper introduces Daubechies 44 (db44) as the most similar mother wavelet function across a variety of biological signals. Three-hundred and twenty four potential mother wavelet functions were selected and investigated in the search for the most similar function. The algorithms were validated by three categories of biological signals: forearm electromyographic (EMG), electroencephalographic (EEG), and vaginal pulse amplitude (VPA). Surface and intramuscular EMG signals were collected from multiple locations on the upper forearm of subjects during ten hand motions. EEG was recorded from three monopolar Ag-AgCl electrodes (Pz, POz, and Oz) during visual stimulus presentation. VPA, a useful source for female sexuality research, were recorded during a study of alcohol and stimuli on sexual behaviors. In this research, after extensive studies on mother wavelet functions, results show that db44 has the most similarity across these classes of biosignals.

Wavelet basis functions in biomedical signal processing

We propose a novel scheme for signal compression based on the discrete wavelet packet transform (DWPT) decompositon. The mother wavelet and the basis of wavelet packets were optimized and the wavelet coefficients were encoded with a modified version of the embedded zerotree algorithm. This signal dependant compression scheme was designed by a two-step process. The first (internal optimization) was the best basis selection that was performed for a given mother wavelet. For this purpose, three additive cost functions were applied and compared. The second (external optimization) was the selection of the mother wavelet based on the minimal distortion of the decoded signal given a fixed compression ratio. The mother wavelet was parameterized in the multiresolution analysis framework by the scaling filter, which is sufficient to define the entire decomposition in the orthogonal case. The method was tested on two sets of ten electromyographic (EMG) and ten electrocardiographic (ECG) signals that were compressed with compression ratios in the range of 50%-90%. For 90% compression ratio of EMG (ECG) signals, the percent residual difference after compression decreased from (mean ) 48.69.9% (21.58.4%) with discrete wavelet transform (DWT) using the wavelet leading to poorest performance to 28.43.0% (6.71.9%) with DWPT, with optimal basis selection and wavelet optimization. In conclusion, best basis selection and optimization of the mother wavelet through parameterization led to substantial improvement of performance in signal compression with respect to DWT and randon selection of the mother wavelet. The method provides an adaptive approach for optimal signal representation for compression and can thus be applied to any type of biomedical signal.

L. Brechet

Papers

Compression of Biomedical Signals With Mother Wavelet Optimization and Best-Basis Wavelet Packet Selection