Patch Beadle

University of Portsmouth, Portsmouth, England, United Kingdom

Are you Patch Beadle?

Claim your profile

Publications (32)0.49 Total impact

  • Lisha Sun, Jun Yu, Patch Beadle
    [Show abstract] [Hide abstract]
    ABSTRACT: Symbolic entropy is proposed to measure the complexity of the electroencephalogram (EEG) signal under different brain functional states. The EEG data recorded from different subjects were investigated and compared with both approximate entropy (ApEn) and Shannon entropy. The experimental results show that the proposed method can effectively distinguish the complexities of two groups. The experimental results provide preliminary support for the notion that the complex nonlinear nature of brain electrical activity may be the result of isolation or impairment of the neural information transmission within the brain. It is concluded that symbolic entropy serves a better measure for EEG signals and other medical signals.
    Advances in Neural Networks - ISNN 2009, 6th International Symposium on Neural Networks, ISNN 2009, Wuhan, China, May 26-29, 2009, Proceedings, Part III; 01/2009
  • Minfen Shen, Jialiang Chen, Patch Beadle
    [Show abstract] [Hide abstract]
    ABSTRACT: To investigate the time-varying characteristics of the multi-channels electroencephalogram (EEG) signals with 4 rhythms, a useful approach is developed to obtain the EEG’s rhythms based on the multi-resolution decomposition of wavelet transformation. Four specified rhythms can be decomposed from EEG signal in terms of wavelet packet analysis. A novel method for time-varying brain electrical activity mapping (BEAM) is also proposed using the time-varying rhythm for visualizing the dynamic EEG topography to help studying the changes of brain activities for one rhythm. Further more, in order to detect the changes of the nonlinear features of the EEG signal, wavelet packet entropy is proposed for this purpose. Both relative wavelet packet energy and wavelet packet entropy are regarded as the quantitative parameter for computing the complexity of the EEG rhythm. Some simulations and experiments using real EEG signals are carried out to show the effectiveness of the presented procedure for clinical use.
    Advances in Neural Networks - ISNN 2009, 6th International Symposium on Neural Networks, ISNN 2009, Wuhan, China, May 26-29, 2009, Proceedings, Part I; 01/2009
  • Lisha Sun, Guoliang Chang, Patch Beadle
    [Show abstract] [Hide abstract]
    ABSTRACT: Method for extracting the specified rhythms of clinical electroencephalogram (EEG) is proposed using the wavelet packet decomposition. Based on the ability of accurately resolving the signal into desired time-frequency components, EEG signals are preprocessed and decomposed into a series of rhythms for many clinical applications. Specified dynamic EEG rhythms can be accurately filtered with designed wavelet structure. In addition, we present a wavelet packet entropy method for processing of EEG signal. Both relative wavelet packet energy and wavelet packet entropy are presented as the quantitative parameter to measure the complexity of the EEG signal. Several experiments with real EEG signals are carried out to show that the proposed method excels the common discrete wavelet decomposition. The presented procedure can isolate specific EEG rhythms accurately and is also regarded as an efficient method for analyzing non-stationary signals in practice.
    Advances in Neural Networks - ISNN 2007, 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part II; 01/2007
  • [Show abstract] [Hide abstract]
    ABSTRACT: EEG signals were expressed as the typical non-stationary signal. More and more evidences were found that both EEG and ERP signals are also chaotic signal from the nonlinear dynamics system. A novel model based on the time-varying coupled map lattice model is proposed for investigating the nonlinear dynamics of EEG under specified cognitive tasks. Moreover, the time-variant largest Lyapunov exponent (LLE) is defined for the purpose of defining quantitative parameters to reveal the global characters of system and extract new information involved in the system. Both simulations and real ERP signals were examined in terms of LLE parameter for studying the signal’s dynamic structure. Several experimental results show that the brain chaos changes with time under different attention tasks of the information processing. The influence of the LLE with the different attention tasks occurs in P2 period.
    05/2006: pages 560-565;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Brain electrical activity is widely accepted as the typical non-stationary signal. In addition, there exist more evidences that both EEG and ERP signals are chaotic signal produced by the nonlinear dynamics system. To investigate the time-varying nonlinear dynamics of the ERP under specified cognitive tasks, a novel model based on the time-variant coupled map lattice system is proposed for this purpose. The time-variant largest Lyapunov exponent (LLE) is also defined as the quantitative parameters to reveal the global characters of system and discover new information. Several simulations and real ERP signals under different fixed location cue were examined in terms of LLE spectra for studying the signal's dynamic complexity. The experimental results show that the brain chaos changes with time under different attention tasks of the information processing. The influence of the LLE with the scopes size of cues mainly occurs in P2 period. No simple linearly relationship between the scopes of cues and the influence of complexity is found.
    01/2006;
  • Source
    Minfen Shen, J. Qiu, Y. Zhang, P.J. Beadle
    [Show abstract] [Hide abstract]
    ABSTRACT: This contribution studies the problem of denoising single-trial visual evoked potentials (VEP) signal. The main objective for VEP detection is to extract the change of the response and the corresponding latency without losing the individual properties of each trial of the signals, which is meaningful to clinic and practical application. Based on the radial basis function neural network (RBFNN), we proposed normalized RBFNN to obtain preferable results against other nonlinear methods: adaptive noise canceling (ANC) with RBFNN prefilter and RBFNN alone. These three approaches are compared with MSE and the ability of tracking peaks. The experimental results provide convergent evidence for the view that NRBFNN significantly attenuates noise and can successfully identify variance between trials
    Neural Networks and Brain, 2005. ICNN&B '05. International Conference on; 11/2005
  • Minfren Shen, Weiling Xu, P.J. Beadle
    [Show abstract] [Hide abstract]
    ABSTRACT: Independent component analysis (ICA) is regarded as a useful technique for processing a wide range of practical signals, such as speech, radar and biomedical recordings. In the biomedical image processing, functional magnetic resonance imaging become a common tool for investigating the brain function and cognitive process. However, much debate on the preferred technique for analyzing these functional activation images is still a problem. In this contribution, blind signal separation via ICA is proposed to detect the brain function activities. Several experiment with digital image data were also carried out based on the presented fastICA algorithm. ICA technique is employed to separate the independent components of the observation and restrain the impact caused by the additive noise. The results using common method and ICA technique were also demonstrated and compared to show that the proposed ICA method significantly reduces the physiological baseline fluctuation and the background interfaces.
    VLSI Design and Video Technology, 2005. Proceedings of 2005 IEEE International Workshop on; 06/2005
  • Lisha Sun, Ying Liu, P.J. Beadle
    [Show abstract] [Hide abstract]
    ABSTRACT: Independent component analysis (ICA) technique is applied to the analysis of electroencephalographic (EEG) signal. The main task of ICA for a random vector includes searching for a linear transformation which minimizes the statistical dependence between the components involved in the signal. In practice, some artifacts problems limit the interpretation and analysis of clinical EEG signals since the rejected contaminated EEG segments results in an unacceptable data loss. In this contribution, ICA filters were trained based on the EEG data during these sessions were identified statistically independent source channels, which could then be further processed using other signal processing techniques. Finally, the applications of ICA to the multichannel EEG recordings from the human brain were investigated and compared. The experimental results indicated that the proposed ICA method for analyzing EEG significantly cancels the additive background noise and separate the mix signals.
    VLSI Design and Video Technology, 2005. Proceedings of 2005 IEEE International Workshop on; 06/2005
  • Ying Liu, Lisha Sun, Yisheng Zhu, Patch Beadle
    [Show abstract] [Hide abstract]
    ABSTRACT: Symbolic dynamics is a useful tool in several fields of complexity analysis in nonlinear science. In order to investigate complexities of the human brain electrical activities under different brain functional states, a novel method in terms of symbolic entropy is defined and proposed in this paper. The novel algorithm based on symbolic dynamics is developed for quantitatively measuring the complexity of the EEG data. Simulated signals derived from chaotic systems and several real EEG data under normal and pathological brain functional states are examined and compared. The experimental results show that the proposed method can distinguish not only the complexities of simulated signals but also the complexities of two groups of EEG data under different brain functional states. It is superior to the traditional entropy methods. Moreover, the algorithm can be easily completed and fast computed.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2005; 1:37-40.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: A novel approach is proposed to deal with the problem of detecting the single trial ERP using a modified RBF neural network, rational Gaussian network. The Gaussian RBF is normalized to obtain optimal behavior of noise suppression even at low SNR. The performance of the proposed scheme is also evaluated with both MSE and the tracking ability. Several experimental results with real ERP signals provide the convergent evidence to show that the presented method can significantly enhance the SNR and successfully track the variation of the signal such as the specified ERP in the applications of biomedical signal processing.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2005; 1:33-6.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: novel parametric method, based on the non-Gaussian AR model, is proposed for the partition of on-stationary EEG data into a finite set of third-order stationary segments. With the assumption of piecewise third-order stationarity of the signal, a series of parametric bispectral estimations of the non-stationary EEG data can be performed so as to describe the time-varying non-Gaussian nonlinear characteristics of the observed EEG signals. A practical method based on the fitness of third-order statistics of the signal by using the non-Gaussian AR model, together with an algorithm with CMI is presented. The experimental results with several simulations and clinical EEG signals have also been investigated and discussed. The results show successful performance of the proposed method in estimating the time-varying bispectral structures of the EEG signals.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2005; 1:829-32.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Digital watermark technology has been developed quickly during the recent few years and widely applied to protect the copyright of digital image. A digital watermark is the information that is imperceptibly and robustly embedded in the host data such that it cannot be removed. This paper proposes a method based on the independent component analysis (ICA) for the digital image watermarking. The experimental results indicate that the presented approach is remarkably effective in detecting and extracting the digital image watermark. The problem of robust watermarking is also tested to demonstrate the effectiveness of the proposed approach.
    Signal Processing, 2004. Proceedings. ICSP '04. 2004 7th International Conference on; 10/2004
  • [Show abstract] [Hide abstract]
    ABSTRACT: A novel method was proposed to addresses the issue of identification of time-varying linear system with non-Gaussian input. A non-Gaussian AR model with time-varying coefficients was developed to track the non-stationary non-Gaussian characteristics of the signal. For system identification and coefficients estimation, each transient model coefficients was expanded onto a finite set of basis sequences. Wavelet basis function was employed so that the model parameters can be effectively tracked and used to estimate the corresponding local parametric bispectrum. Finally, the performance of the proposed approach was assessed with some simulations. The experimental results show the flexibility and the effectiveness of the presented method.
    Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on; 07/2004
  • Minfen Shen, Qianhua Zhang, P.J. Beadle
    [Show abstract] [Hide abstract]
    ABSTRACT: The application of a measurement of entropy defined from the nonextensive entropy, the Tsallis-like time-dependent entropy (TDE), is proposed to investigate the event-related potential (ERPs). The TDE carries information about the degree of order or disorder associated with a multi-frequency signal response. The statistical characteristics of the TDE for different signal distributions are studied. TDE was estimated for the ERP signals recorded from several healthy subjects with a specified cognitive task. A significant decrease of entropy was correlated with the responses to target stimulus (P300). The experimental results indicate that the TDE can be employed as a quantitative measurement for monitoring the ERPs activities and other signal processing.
    Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on; 01/2004
  • Minfen Shen, J. Huang, P.J. Beadle
    [Show abstract] [Hide abstract]
    ABSTRACT: Digital watermark processing technology has developed very quickly during the recent few years, and been widely applied to protect the copyright of digital image, audio, video and multimedia production. In this paper, a method based on the independent component analysis (ICA) technique for detection and extraction of digital image watermark is proposed. With ICA techniques, it is ensured that a better-extracted watermark can be obtained. Several results of experiments indicate the proposed method is remarkably effective.
    Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on; 01/2004
  • [Show abstract] [Hide abstract]
    ABSTRACT: A basic scheme for extracting digital image watermark is proposed using independent component analysis (ICA). The algorithm in terms of fastICA is discussed and used to separate the watermark from the mixed sources. The behavior of the proposed approach with several robustness tests of the image watermark is also carried out to demonstrate that ICA technique could provide a flexible and robust system for performing digital watermark detection and extraction. The preliminary experimental results show that the proposed watermarking method is effective and robust to some possible attacks.
    AI 2004: Advances in Artificial Intelligence, 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, December 4-6, 2004, Proceedings; 01/2004
  • [Show abstract] [Hide abstract]
    ABSTRACT: Hilbert spectrum was employed to investigate the time-varying frequency characteristics of the practical heart sound signals. The aim is to explore the role that both empirical mode decomposition and Hilbert transform can be used to play in such heart sound signals. Hilbert transform is applied to each intrinsic mode function to obtain the global time-frequency distribution of the underlying signal with a point of view of instantaneous frequency. The instantaneous frequency distributions of heart sound signals were also compared with the results by using the wavelet transform. Both simulation and experimental results were presented and discussed to demonstrate the power and effectiveness of the proposed new approach.
    Signal Processing, 2004. Proceedings. ICSP '04. 2004 7th International Conference on; 01/2004
  • Zhancheng Li, Minfen Shen, Patch Beadle
    [Show abstract] [Hide abstract]
    ABSTRACT: Investigation of the states of human brain through the elec-troencephalograph (EEG) is an important application of EEG signals. This paper describes the application of an artificial neural network technique together with a feature extraction technique, the wavelet packet transformation, in classification of EEG signals. Feature vector is extracted by wavelet packet transform. Artificial neural network is used to recognize the brain statues. After training, the BP and RBF neural network are able to correctly classify the brain states, respectively. This method is potentially powerful for brain states classification.
    Advances in Neural Networks - ISNN 2004, International Symposium on Neural Networks, Dalian, China, August 19-21, 2004, Proceedings, Part II; 01/2004
  • [Show abstract] [Hide abstract]
    ABSTRACT: A novel approach is proposed to solve the problem of detecting the signal in the noise using a modified RBF neural network (RBFNN). The RBFNN is normalized to obtain optimal behavior of noise suppression even at low SNR. The performance of the proposed scheme is also evaluated with both MSE and the tracking ability. Several experimental results provide the convergent evidence to show that the method can significantly enhance the SNR and successfully track the variation of the signal such as evoket potential.
    Parallel and Distributed Computing: Applications and Technologies, 5th International Conference, PDCAT 2004, Singapore, December 8-10, 2004, Proceedings; 01/2004
  • L. Sun, S. Wang, M. Shen, P.J. Beadle
    [Show abstract] [Hide abstract]
    ABSTRACT: A novel method was proposed to addresses the issue of identification of time-varying linear system with non-Guussian input, A non-Gaussian AR model with time-varying coefficients was developed to track the non-stationary non-Gaussian characteristics of the signal. For system identification and coefficients estimation, each transient model coefficients was expanded onto a finite set of basis sequences. Wavelet basis function was employed so that the model parameters can be effectively tracked and used to estimate the corresponding local parametric bispectrum. Finally, the performance of the proposed approach was assessed with some simulations. The experimental results show the flexibility and the effectiveness of the presented method.
    Signal Processing, 2004. Proceedings. ICSP '04. 2004 7th International Conference on; 01/2004