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ABSTRACT: The 'empirical mode decomposition' (EMD) method has been recently proposed to deal with nonlinear and non- stationary data, which decomposes signals into 'well-behaved' intrinsic mode functions (IMFs). An assessment on the qualitative performance of the EMD method in terms of the degree of signal nature preservation of individual IMF is provided. This is archived by means of the recently proposed signal characterisation method, based upon examining the signal predicability in phase space. It is shown that the first IMF always performs best in terms of signal nature preserving. Simulation results on both linear and nonlinear benchmark signals support the analysis.
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on; 05/2008 · 4.63 Impact Factor
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ABSTRACT: A novel method for the online detection of the modality of complex-valued nonlinear and nonstationary signals is introduced. This is achieved using a convex combination of complex nonlinear adaptive filters with different transient characteristics. To facilitate the online mode of operation, the convex mixing parameter lambda within the proposed architecture is made gradient adaptive. Our focus is on the most important aspect of complex nonlinear modeling, that is, the identification of the split-complex and fully-complex nature of the signal in hand. The algorithms derived are robust and capable of tracking the changes in the modality of both benchmark and real world radar and wind complex vector fields.
International Journal of Neural Systems 05/2008; 18(2):67-74. · 4.28 Impact Factor
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ABSTRACT: A framework for the assessment of qualitative performance of machine learning architectures is proposed. For generality, the analysis is provided for the modular nonlinear pipelined recurrent neural network (PRNN) architecture. This is supported by a sensitivity analysis, which is achieved based upon the prediction performance with respect to changes in the nature of the processed signal and by utilizing the recently introduced delay vector variance (DVV) method for phase space signal characterization. Comprehensive simulations combining the quantitative and qualitative analysis on both linear and nonlinear signals suggest that better quantitative prediction performance may need to be traded in order to preserve the nature of the processed signal, especially where the signal nature is of primary importance (biomedical applications).
IEEE Transactions on Neural Networks 02/2008; 19(1):183-9. · 2.95 Impact Factor
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Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2008, March 30 - April 4, 2008, Caesars Palace, Las Vegas, Nevada, USA; 01/2008
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ABSTRACT: Novel nonlinear dynamical analysis of the electroencephalogram (EEG) data recorded in steady state brain stimulation paradigms is provided. This is achieved based on some recent developments in the local predictability in phase space, which allows for the assessment of the degree of nonlinearity and uncertainty within the EEG data. Both the responses from the visual and auditory experiments are addressed, based on the auditory steady-state responses (ASSR) and steady-state visual evoked potentials (SSVEP). Simulation results show clear difference in the degree of nonlinearity and uncertainty between the segments of EEG data recorded before, during and after the stimulus. This provides a novel insight into the dynamics of the brain information processing mechanism captured in EEG.
Machine Learning for Signal Processing, 2007 IEEE Workshop on; 09/2007
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Proceedings of The 2007 RISP International Workshop on Nonlinear Circuits and Signal Processing; 01/2007
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ABSTRACT: A combination of linear and nonlinear methods for feature fusion is introduced and the performance of this methodology is illustrated on a real-world problem: the detection of sudden and non-anticipated lapses of attention in car drivers due to drowsiness. To achieve this, signals coming from heterogeneous sources are processed, namely the brain electric activity, variation in the pupil size, and eye and eyelid movements. For all the signals considered, the features are extracted both in the spectral domain and in state space. Linear features are obtained by the modified periodogram, whereas the nonlinear features are based on the recently introduced method of delay vector variance (DVV). The decision process based on such fused features is achieved by support vector machines (SVM) and learning vector quantization (LVQ) neural networks. For the latter also methods of metrics adaptation in the input space are applied. The parameters of all utilized algorithms are optimized empirically in order to gain maximal classification accuracy. It is also shown that metrics adaptation by weighting the input features can improve the classification accuracy, but only to a limited extent. Limited improvements are also obtained when fusing features of selected signals, but highest improvements are gained by fusion of features of all available signals. In this case test errors are reduced down to 9% in the mean, which clearly illustrates the potential of our methodology to establish a reference standard of drowsiness and microsleep detection devices for future online driver monitoring.
Journal of VLSI Signal Processing 01/2007; 49:329-342. · 0.73 Impact Factor
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Advances in Nonlinear Speech Processing, International Conference on Non-Linear Speech Processing, NOLISP 2007, Paris, France, May 22-25, 2007, Revised Selected Papers; 01/2007
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VLSI Signal Processing. 01/2007; 49:329-342.
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ABSTRACT: A new framework for the assessment of the qualitative performance of Kalman filter is proposed. This is achieved by the recently proposed `Delay Vector Variance' (DVV) method for the signal modality characterisation, which is based upon the local predictability in the phase space. It is shown that Kalman filter not only outperforms common linear and non-linear filters in terms of quantitative performance but also achieves a better qualitative performance. A set of comprehensive simulations on representative data sets supports the analysis.
Nonlinear Statistical Signal Processing Workshop, 2006 IEEE; 10/2006
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ABSTRACT: A framework for automated scoring of sleep stages during afternoon naps of healthy humans is introduced. This is achieved by sequential fusion of nonlinear features extracted from three physiological channels: the electroencephalogram (EEG), electrooculogram (EOG) and respiratory trace (RES). These features are generated by means of the recently introduced "delay vector variance" (DVV) method which examines local predictability of a signal in phase space. The analysis is accompanied by a set of comprehensive simulations, supporting the approach
Multisensor Fusion and Integration for Intelligent Systems, 2006 IEEE International Conference on; 10/2006
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Knowledge-Based Intelligent Information and Engineering Systems, 10th International Conference, KES 2006, Bournemouth, UK, October 9-11, 2006, Proceedings, Part III; 01/2006
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Artificial Neural Networks: Formal Models and Their Applications - ICANN 2005, 15th International Conference, Warsaw, Poland, September 11-15, 2005, Proceedings, Part II; 01/2005
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ABSTRACT: Traditionally, research on adaptive signal processing has been conducted with the aim of designing adaptive filters with high performance in terms of some prescribed performance measure. However, little is known about how such filters influence the nature of the processed signal. Based upon some recently introduced results in dealing with nonlinearity within a signal in hand, we provide a critical assessment of the qualitative performance of common linear and nonlinear filters and their combinations. An insight into the performance of so called hybrid filters is provided, which is achieved for combinations of standard nonlinear (neural) and linear filters. It is shown that depending on the application, it is important not only to look for best filter performance in terms of some quantitative measure of the error but also for a filter that will not change the character of a signal. Simulation results support the analysis.
Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on; 06/2004 · 4.63 Impact Factor
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ABSTRACT: We provide an signal modality analysis on the heart rate variability (HRV) data, widely studied as an indication of the health status of the heart. The analysis is achieved by using the recently proposed 'delay vector variance' (DVV) method, which rests upon examining the local predictability of a signal in the phase space. A comprehensive analysis of the feasibility of this approach is provided. The simulation results show that the DVV method can be opted for an alternative way to help doctors diagnose the patients with heart disease.
