Wavelet basis functions in biomedical signal processing

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.

[1]  Martha Flanders,et al.  Choosing a wavelet for single-trial EMG , 2002, Journal of Neuroscience Methods.

[2]  M. Sabarimalai Manikandan,et al.  Wavelet threshold based TDL and TDR algorithms for real-time ECG signal compression , 2008, Biomed. Signal Process. Control..

[3]  Brij N. Singh,et al.  Optimal selection of wavelet basis function applied to ECG signal denoising , 2006, Digit. Signal Process..

[4]  Kevin B. Englehart,et al.  A wavelet-based continuous classification scheme for multifunction myoelectric control , 2001, IEEE Transactions on Biomedical Engineering.

[5]  Levi J. Hargrove,et al.  A Comparison of Surface and Intramuscular Myoelectric Signal Classification , 2007, IEEE Transactions on Biomedical Engineering.

[6]  Yan Guozheng,et al.  EEG feature extraction based on wavelet packet decomposition for brain computer interface , 2008 .

[7]  Irwin Goldstein,et al.  Summary of the recommendations on sexual dysfunctions in women. , 2004, The journal of sexual medicine.

[8]  K. I. Ramachandran,et al.  A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box , 2008, Expert Syst. Appl..

[9]  Pei-wen Que,et al.  Optimal scale wavelet transform for the identification of weak ultrasonic signals , 2009 .

[10]  Marie-Françoise Lucas,et al.  Optimized Wavelets for Blind Separation of Nonstationary Surface Myoelectric Signals , 2008, IEEE Transactions on Biomedical Engineering.

[11]  Taha Landolsi.  Accuracy of the split-step wavelet method using various wavelet families in simulating optical pulse propagation , 2006, J. Frankl. Inst..

[12]  J. Geer,et al.  Sexual arousal in women: The development of a measurement device for vaginal blood volume , 1974, Archives of sexual behavior.

[13]  H. Hermens,et al.  European recommendations for surface electromyography: Results of the SENIAM Project , 1999 .

[14]  Marie-Françoise Lucas,et al.  Optimization of wavelets for classification of movement-related cortical potentials generated by variation of force-related parameters , 2007, Journal of Neuroscience Methods.

[15]  M. Riera-Guasp,et al.  Validation of a new method for the diagnosis of rotor bar failures via wavelet transform in industrial induction machines , 2006, IEEE Transactions on Industry Applications.

[16]  Huosheng Hu,et al.  Myoelectric control systems - A survey , 2007, Biomed. Signal Process. Control..

[17]  A. Cohen.  Ten Lectures on Wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics, Vol. 61, I. Daubechies, SIAM, 1992, xix + 357 pp. , 1994 .

[18]  Erick Janssen,et al.  Blood flow: Vaginal photoplethysmography , 2006 .

[19]  A. Evers,et al.  Assessment of female sexual arousal: response specificity and construct validity. , 1995, Psychophysiology.

[20]  E. Basar,et al.  Wavelet entropy: a new tool for analysis of short duration brain electrical signals , 2001, Journal of Neuroscience Methods.

[21]  Abdulhamit Subasi,et al.  Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients , 2005, Expert Syst. Appl..

[22]  John Dean,et al.  Summary of the recommendations on sexual dysfunctions in men. , 2004, The journal of sexual medicine.

[23]  Peter W. Tse,et al.  Use of autocorrelation of wavelet coefficients for fault diagnosis , 2009 .

[24]  P. Tse,et al.  Machine fault diagnosis through an effective exact wavelet analysis , 2004 .

[25]  J. Rafiee,et al.  Feature extraction of forearm EMG signals for prosthetics , 2011, Expert Syst. Appl..

[26]  Erick Janssen,et al.  Attention and Emotional Responses to Sexual Stimuli and Their Relationship to Sexual Desire , 2008, Archives of sexual behavior.

[27]  I. Daubechies.  Orthonormal bases of compactly supported wavelets , 1988 .

[28]  Javad Rafiee,et al.  Female sexual responses using signal processing techniques. , 2009, The journal of sexual medicine.

[29]  Mehmet Engin,et al.  Feature measurements of ECG beats based on statistical classifiers , 2007 .

[30]  Necmettin Sezgin,et al.  The ANN-based computing of drowsy level , 2009, Expert Syst. Appl..

[31]  Marie-Françoise Lucas,et al.  Compression of Biomedical Signals With Mother Wavelet Optimization and Best-Basis Wavelet Packet Selection , 2007, IEEE Transactions on Biomedical Engineering.

[32]  Candace S. Brown,et al.  The Female Sexual Function Index (FSFI): a multidimensional self-report instrument for the assessment of female sexual function. , 2000, Journal of sex & marital therapy.

[33]  Marie-Françoise Lucas,et al.  Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization , 2008, Biomed. Signal Process. Control..

[34]  I. Daubechies.  Ten Lectures on Wavelets , 1992 .

[35]  H. Hermens,et al.  SENIAM 8: European recommendations for surface electromyography , 1999 .