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B. Boashash - One of the best experts on this subject based on the ideXlab platform.
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an improved design of high resolution quadratic time frequency distriButions for the analysis of nonstationary multicomponent signals using directional compact kernels
IEEE Transactions on Signal Processing, 2017Co-Authors: B. Boashash, Samir OuelhaAbstract:This paper presents a new advanced methodology for designing high resolution time–frequency distriButions (TFDs) of multicomponent nonstationary signals that can Be approximated using piece-wise linear frequency modulated (PW-LFM) signals. Most previous kernel design methods assumed that signals auto-terms are mostly centered around the origin of the $(\nu,\tau)$ amBiguity domain while signal cross-terms are mostly away from the origin. This study uses a multicomponent test signal for which each component is modeled as a PW-LFM signal; it finds that the aBove assumption is a very rough approximation of the location of the auto-terms energy and cross-terms energy in the amBiguity domain and it is only valid for signals that are well separated in the $(t,f)$ domain. A refined investigation led to improved specifications for separating cross-terms from auto-terms in the $(\nu,\tau)$ amBiguity domain. The resulting approach first represents the signal in the amBiguity domain, and then applies a multidirectional signal dependent compact kernel that accounts for the direction of the auto-terms energy. The resulting multidirectional distriBution (MDD) approach proves to Be more effective than classical methods like extended modified B distriBution, S-method, or compact kernel distriBution in terms of auto-terms resolution and cross-terms suppression. Results on simulated and real data validate the improved performance of the MDD, showing up to 8% gain as compared to more standard state-of-the-art TFDs.
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a review of time frequency matched filter design with application to seizure detection in multichannel newBorn eeg
Digital Signal Processing, 2014Co-Authors: B. Boashash, Ghasem AzemiAbstract:This paper presents a novel design of a time-frequency (t-f) matched filter as a solution to the proBlem of detecting a non-stationary signal in the presence of additive noise, for application to the detection of newBorn seizure using multichannel EEG signals. The solution reduces to two possiBle t-f approaches that use a general formulation of t-f matched filters (TFMFs) Based on the Wigner-Ville and cross Wigner-Ville distriButions, and a third new approach Based on the signal amBiguity domain representation; referred to as Radon-amBiguity detector. This contriBution defines a general design formulation and then implements it for newBorn seizure detection using multichannel EEG signals. Finally, the performance of different TFMFs is evaluated for different t-f kernels in terms of classification accuracy using real newBorn EEG signals. Experimental results show that the detection method which uses TFMFs Based on the cross Wigner-Ville distriBution outperforms other approaches including the existing TFMF-Based ones. The results also show that TFMFs which use high-resolution kernels such as the modified B-distriBution, achieve higher detection accuracies compared to the ones which use other reduced-interference t-f kernels.
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A comparison of quadratic TFDs for entropy Based detection of components time supports in multicomponent nonstationary signal mixtures
2013 8th International Workshop on Systems Signal Processing and their Applications (WoSSPA), 2013Co-Authors: Nicoletta Saulig, B. Boashash, Damir SersicAbstract:Separation of different signal components, produced By one or more sources, is a proBlem encountered in many signal processing applications. This paper proposes a fully automatic undetermined Blind source separation method, Based on a peak detection and extraction technique from a signal time-frequency distriBution (TFD). Information on the local numBer of components is oBtained from the TFD Short-term Rényi entropy. It also allows to detect components time supports in the time-frequency plane, with no need for predefined thresholds on the components amplitude. This approach allows to extract different signal components without prior knowledge aBout the signal. The method is also used as a quality criterion to compare Quadratic TFDs. Results for synthetic and real data are reported for different TFDs, including the recently introduced Extended Modified B distriBution.
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a comparison of quadratic tfds for entropy Based detection of components time supports in multicomponent nonstationary signal mixtures
International Workshop on Systems Signal Processing and their Applications, 2013Co-Authors: Nicoletta Saulig, B. Boashash, Damir SersicAbstract:Separation of different signal components, produced By one or more sources, is a proBlem encountered in many signal processing applications. This paper proposes a fully automatic undetermined Blind source separation method, Based on a peak detection and extraction technique from a signal time-frequency distriBution (TFD). Information on the local numBer of components is oBtained from the TFD Short-term Renyi entropy. It also allows to detect components time supports in the time-frequency plane, with no need for predefined thresholds on the components amplitude. This approach allows to extract different signal components without prior knowledge aBout the signal. The method is also used as a quality criterion to compare Quadratic TFDs. Results for synthetic and real data are reported for different TFDs, including the recently introduced Extended Modified B distriBution.
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Design of a high-resolution separaBle-kernel quadratic TFD for improving newBorn health outcomes using fetal movement detection
2012 11th International Conference on Information Science Signal Processing and their Applications (ISSPA), 2012Co-Authors: B. Boashash, Taoufik Ben-jabeurAbstract:Prior to Birth, fetus health can Be monitored By the variety and scale of its movements. In addition, at Birth, EEG signals are recorded from at-risk newBorns. Studies have shown that Both fetal movements and newBorn EEGs are non-stationary signals. This paper aims to represent Both newBorn EEG and fetal movement signals in a time-frequency domain using a specifically designed time-frequency distriBution (TFD) that is well adapted to these types of data for the purpose of analysis, detection and classification. The approach to design the quadratic TFDS is Based on relating separaBle-kernel TFDS to DSP spectral window and digital filter design. To reach this goal, we compared recently proposed TFDs such as the Modified B distriBution, a separaBle Gaussian distriBution and the B distriBution. Then, an extension of the modified B distriBution (MBD) is proposed, referred to as the extended separaBle-kernel MBD. This new TFD uses a separaBle kernel Based on an extension of the modified B kernel in Both time and frequency domain with different windows for each domain. Simulation results are provided to compare the proposed Method with different TFDs and to assess its performance. The new TFD is then first applied to real fetal movement data recorded using accelerometers.
P. A. Karthick - One of the best experts on this subject based on the ideXlab platform.
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surface electromyography Based muscle fatigue detection using high resolution time frequency methods and machine learning algorithms
Computer Methods and Programs in Biomedicine, 2018Co-Authors: P. A. Karthick, Diptasree Maitra Ghosh, S. RamakrishnanAbstract:ABstract Background and oBjective Surface electromyography (sEMG) Based muscle fatigue research is widely preferred in sports science and occupational/rehaBilitation studies due to its noninvasiveness. However, these signals are complex, multicomponent and highly nonstationary with large inter-suBject variations, particularly during dynamic contractions. Hence, time-frequency Based machine learning methodologies can improve the design of automated system for these signals. Methods In this work, the analysis Based on high-resolution time-frequency methods, namely, Stockwell transform (S-transform), B-distriBution (BD) and extended modified B-distriBution (EMBD) are proposed to differentiate the dynamic muscle nonfatigue and fatigue conditions. The nonfatigue and fatigue segments of sEMG signals recorded from the Biceps Brachii of 52 healthy volunteers are preprocessed and suBjected to S-transform, BD and EMBD. Twelve features are extracted from each method and prominent features are selected using genetic algorithm (GA) and Binary particle swarm optimization (BPSO). Five machine learning algorithms, namely, naive Bayes, support vector machine (SVM) of polynomial and radial Basis kernel, random forest and rotation forests are used for the classification. Results The results show that all the proposed time-frequency distriButions (TFDs) are aBle to show the nonstationary variations of sEMG signals. Most of the features exhiBit statistically significant difference in the muscle fatigue and nonfatigue conditions. The maximum numBer of features (66%) is reduced By GA and BPSO for EMBD and BD-TFD respectively. The comBination of EMBD- polynomial kernel Based SVM is found to Be most accurate (91% accuracy) in classifying the conditions with the features selected using GA. Conclusions The proposed methods are found to Be capaBle of handling the nonstationary and multicomponent variations of sEMG signals recorded in dynamic fatiguing contractions. Particularly, the comBination of EMBD- polynomial kernel Based SVM could Be used to detect the dynamic muscle fatigue conditions.
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surface electromyography Based muscle fatigue progression analysis using modified B distriBution time frequency features
Biomedical Signal Processing and Control, 2016Co-Authors: P. A. Karthick, S. RamakrishnanAbstract:ABstract In this work, an attempt has Been made to analyze the progression of muscle fatigue using surface electromyography (sEMG) signals and modified B distriBution (MBD) Based time–frequency analysis. For this purpose, signals are recorded from Biceps Brachii muscles of fifty healthy adult volunteers during dynamic contractions. The recorded signals are preprocessed and then suBjected to MBD Based time–frequency distriBution (TFD). The instantaneous median frequency (IMDF) is extracted from the time–frequency matrix for different values of kernel parameter. The linear regression technique is used to model the temporal variations of IMDF. Correlation coefficient is computed in order to select the appropriate value for kernel parameter of MBD Based TFD. Further, extended version of frequency domain features namely instantaneous spectral ratio (InstSPR) at low frequency Band (LFB), medium frequency Band (MFB) and high frequency Band (HFB) are extracted from the time–frequency spectrum. In addition to these features, IMDF and instantaneous mean frequency (IMNF) are also calculated. The least square error Based linear regression technique is used to track the slope variations of these features. The results show that MBD Based time–frequency spectrum is aBle to provide the instantaneous variations of frequency components associated with fatiguing contractions. The values of InstSPR at MFB and HFB regions, IMDF and IMNF show a decreasing trend during the progression of muscle fatigue. However, an increasing trend is oBserved in LFB regions. Further the coefficient of variation is calculated for all the features. It is found that the values of IMDF, IMNF and InstSPR in LFB region have lowest variaBility across different suBjects in comparison with other two features. It appears that this method could Be useful in analyzing various neuromuscular activities in normal and aBnormal conditions.
S. Ramakrishnan - One of the best experts on this subject based on the ideXlab platform.
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surface electromyography Based muscle fatigue detection using high resolution time frequency methods and machine learning algorithms
Computer Methods and Programs in Biomedicine, 2018Co-Authors: P. A. Karthick, Diptasree Maitra Ghosh, S. RamakrishnanAbstract:ABstract Background and oBjective Surface electromyography (sEMG) Based muscle fatigue research is widely preferred in sports science and occupational/rehaBilitation studies due to its noninvasiveness. However, these signals are complex, multicomponent and highly nonstationary with large inter-suBject variations, particularly during dynamic contractions. Hence, time-frequency Based machine learning methodologies can improve the design of automated system for these signals. Methods In this work, the analysis Based on high-resolution time-frequency methods, namely, Stockwell transform (S-transform), B-distriBution (BD) and extended modified B-distriBution (EMBD) are proposed to differentiate the dynamic muscle nonfatigue and fatigue conditions. The nonfatigue and fatigue segments of sEMG signals recorded from the Biceps Brachii of 52 healthy volunteers are preprocessed and suBjected to S-transform, BD and EMBD. Twelve features are extracted from each method and prominent features are selected using genetic algorithm (GA) and Binary particle swarm optimization (BPSO). Five machine learning algorithms, namely, naive Bayes, support vector machine (SVM) of polynomial and radial Basis kernel, random forest and rotation forests are used for the classification. Results The results show that all the proposed time-frequency distriButions (TFDs) are aBle to show the nonstationary variations of sEMG signals. Most of the features exhiBit statistically significant difference in the muscle fatigue and nonfatigue conditions. The maximum numBer of features (66%) is reduced By GA and BPSO for EMBD and BD-TFD respectively. The comBination of EMBD- polynomial kernel Based SVM is found to Be most accurate (91% accuracy) in classifying the conditions with the features selected using GA. Conclusions The proposed methods are found to Be capaBle of handling the nonstationary and multicomponent variations of sEMG signals recorded in dynamic fatiguing contractions. Particularly, the comBination of EMBD- polynomial kernel Based SVM could Be used to detect the dynamic muscle fatigue conditions.
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surface electromyography Based muscle fatigue progression analysis using modified B distriBution time frequency features
Biomedical Signal Processing and Control, 2016Co-Authors: P. A. Karthick, S. RamakrishnanAbstract:ABstract In this work, an attempt has Been made to analyze the progression of muscle fatigue using surface electromyography (sEMG) signals and modified B distriBution (MBD) Based time–frequency analysis. For this purpose, signals are recorded from Biceps Brachii muscles of fifty healthy adult volunteers during dynamic contractions. The recorded signals are preprocessed and then suBjected to MBD Based time–frequency distriBution (TFD). The instantaneous median frequency (IMDF) is extracted from the time–frequency matrix for different values of kernel parameter. The linear regression technique is used to model the temporal variations of IMDF. Correlation coefficient is computed in order to select the appropriate value for kernel parameter of MBD Based TFD. Further, extended version of frequency domain features namely instantaneous spectral ratio (InstSPR) at low frequency Band (LFB), medium frequency Band (MFB) and high frequency Band (HFB) are extracted from the time–frequency spectrum. In addition to these features, IMDF and instantaneous mean frequency (IMNF) are also calculated. The least square error Based linear regression technique is used to track the slope variations of these features. The results show that MBD Based time–frequency spectrum is aBle to provide the instantaneous variations of frequency components associated with fatiguing contractions. The values of InstSPR at MFB and HFB regions, IMDF and IMNF show a decreasing trend during the progression of muscle fatigue. However, an increasing trend is oBserved in LFB regions. Further the coefficient of variation is calculated for all the features. It is found that the values of IMDF, IMNF and InstSPR in LFB region have lowest variaBility across different suBjects in comparison with other two features. It appears that this method could Be useful in analyzing various neuromuscular activities in normal and aBnormal conditions.
Zhiyong Zhang - One of the best experts on this subject based on the ideXlab platform.
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diagnostics of roBust growth curve modeling using student s t distriBution
Multivariate Behavioral Research, 2012Co-Authors: Xin Tong, Zhiyong ZhangAbstract:Growth curve models with different types of distriButions of random effects and of intraindividual measurement errors for roBust analysis are compared. After demonstrating the influence of distriBution specification on parameter estimation, 3 methods for diagnosing the distriButions for Both random effects and intraindividual measurement errors are proposed and evaluated. The methods include (a) distriBution checking Based on individual growth curve analysis; (B) distriBution comparison Based on Deviance Information Criterion, and (c) post hoc checking of degrees of freedom estimates for t distriButions. The performance of the methods is compared through simulation studies. When the sample size is reasonaBly large, the method of post hoc checking of degrees of freedom estimates works Best. A weB interface is developed to ease the use of the 3 methods. Application of the 3 methods is illustrated through growth curve analysis of mathematical aBility development using data on the PeaBody Individual Achieveme...
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diagnostics of roBust growth curve modeling using student s t distriBution
Multivariate Behavioral Research, 2012Co-Authors: Xin Tong, Zhiyong ZhangAbstract:Growth curve models with different types of distriButions of random effects and of intraindividual measurement errors for roBust analysis are compared. After demonstrating the influence of distriBution specification on parameter estimation, 3 methods for diagnosing the distriButions for Both random effects and intraindividual measurement errors are proposed and evaluated. The methods include (a) distriBution checking Based on individual growth curve analysis; (B) distriBution comparison Based on Deviance Information Criterion, and (c) post hoc checking of degrees of freedom estimates for t distriButions. The performance of the methods is compared through simulation studies. When the sample size is reasonaBly large, the method of post hoc checking of degrees of freedom estimates works Best. A weB interface is developed to ease the use of the 3 methods. Application of the 3 methods is illustrated through growth curve analysis of mathematical aBility development using data on the PeaBody Individual Achievement Test Mathematics assessment from the National Longitudinal Survey of Youth 1997 Cohort (Bureau of LaBor Statistics, U.S. Department of LaBor, 2005).
Viktor Sucic - One of the best experts on this subject based on the ideXlab platform.
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resolution performance assessment for quadratic tfds
TIme-Frequency Signal Analysis and Processing: A Comprehensive Reference, 2003Co-Authors: B. Boashash, Viktor SucicAbstract:Quadratic time-frequency distriButions (TFDs) are effective tools for extracting information from a non-stationary signal, such as the numBer of components, their durations and Bandwidths, components’ relative amplitudes and instantaneous frequency (IF) laws (see Chapters 1 and 2). The performance of TFDs depends on the type of signal (see Chapter 3) [1,2]. For example, in the case of a monocomponent linear FM signal, the Wigner-Ville distriBution is known to Be optimal in the sense that it achieves the Best energy concentration around the signal IF law (see Article 2.1 for more details) [1]. In applications involving multicomponent signals, choosing the right TFD to analyze the signals is an immediate critical task for the signal analyst. How Best to make this assessment, using current knowledge, is the suBject of this article. Let us, for example, consider a multicomponent whale signal, represented in the time-frequency domain using the Wigner-Ville distriBution, the spectrogram, the Choi-Williams distriBution, the Born-Jordan distriBution, the Zhao-Atlas-Marks (ZAM) distriBution, and the recently introduced B-distriBution [3] (see Fig. 7.4.1). To determine which of the TFDs in Fig. 7.4.1 “Best” represents this whale signal (i.e. which one gives the Best components’ energy concentration and Best interference terms suppression, and allows the Best estimation of the components’ IF laws) one could visually compare the six plots and choose the most appealing. The spectrogram and the B-distriBution, Being almost free from the cross-terms, seem to perform Best. The performance comparison Based on the visual inspection of the plots Becomes more difficult and unreliaBle, however, when the signal components are closelyspaced in the time-frequency plane. To oBjectively compare the plots in Fig. 7.4.1 requires to use a quantitative performance measure for TFDs. There have Been several attempts to define oBjective measures of “complexity” for TFDs (see Section 7.3.1). One of these measures, the Renyi entropy given in [4], has Been used By several authors in preference to e.g. the Bandwidth–duration product given in [1]. The performance measure descriBed in this article, unlike the Renyi entropy, is a local measure of the TFD resolution performance, and is thus more suited to the selection proBlem illustrated By Fig. 7.4.1. This measure takes into account the characteristics of TFDs that influence their resolution, such as energy concentration, components separation, and interference terms minimization. Methodologies for choosing a TFD which Best suits a given signal can then Be developed By optimizing the resolution performance of considered TFDs and modifying their parameters to Better match application-specific requirements.
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Resolution measure criteria for the oBjective assessment of the performance of quadratic time-frequency distriButions
IEEE Transactions on Signal Processing, 2003Co-Authors: B. Boashash, Viktor SucicAbstract:This paper presents the essential elements for developing oBjective methods of assessment of the performance of time-frequency signal analysis techniques. We define a measure for assessing the resolution performance of time-frequency distriButions (TFDs) in separating closely spaced components in the time-frequency domain. The measure takes into account key attriButes of TFDs, such as components mainloBes and sideloBes and cross-terms. The introduction of this measure allows to quantify the quality of TFDs instead of relying solely on visual inspection of their plots. The method of assessment of performance of TFDs also allows the improvement of methodologies for designing high-resolution quadratic TFDs for time-frequency analysis of multicomponent signals. Different TFDs, including the modified B distriBution, are optimized using this methodology. Examples of a performance comparison of quadratic TFDs in resolving closely spaced components in the time-frequency domain, using the proposed resolution measure, are provided.
