Distribution entropy analysis of epileptic EEG signals

It is an open-ended challenge to accurately detect the epileptic seizures through electroencephalogram (EEG) signals. Recently published studies have made elaborate attempts to distinguish between the normal and epileptic EEG signals by advanced nonlinear entropy methods, such as the approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, etc. Most recently, a novel distribution entropy (DistEn) has been reported to have superior performance compared with the conventional entropy methods for especially short length data. We thus aimed, in the present study, to show the potential of DistEn in the analysis of epileptic EEG signals. The publicly-accessible Bonn database which consisted of normal, interictal, and ictal EEG signals was used in this study. Three different measurement protocols were set for better understanding the performance of DistEn, which are: i) calculate the DistEn of a specific EEG signal using the full recording; ii) calculate the DistEn by averaging the results for all its possible non-overlapped 5 second segments; and iii) calculate it by averaging the DistEn values for all the possible non-overlapped segments of 1 second length, respectively. Results for all three protocols indicated a statistically significantly increased DistEn for the ictal class compared with both the normal and interictal classes. Besides, the results obtained under the third protocol, which only used very short segments (1 s) of EEG recordings showed a significantly (p <; 0.05) increased DistEn for the interictal class in compassion with the normal class, whereas both analyses using relatively long EEG signals failed in tracking this difference between them, which may be due to a nonstationarity effect on entropy algorithm. The capability of discriminating between the normal and interictal EEG signals is of great clinical relevance since it may provide helpful tools for the detection of a seizure onset. Therefore, our study suggests that the DistEn analysis of EEG signals is very promising for clinical and even portable EEG monitoring.

A modified CM algorithm for blind equalization of MPSK signals

A new blind equalization algorithm for application to wireless communication employing MPSK signals is proposed in this paper.  Since the new cost function exploits the amplitude and phase information simultaneously, the proposed algorithm can provide a superior performance than the conventional constant modulus algorithm (CMA) which only use the amplitude knowledge in its cost function.  Theoretical analysis and numerical simulations both demonstrate that the steady-state mean square error (MSE) for the proposed algorithm is less than that of the CMA.

A neural networks learning algorithm for minor component analysis and its convergence analysis

The eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of input signals is called minor component. Minor component analysis (MCA) is a statistical approach for extracting minor component from input signals and has been applied in many fields of signal processing and data analysis. In this letter, we propose a neural networks learning algorithm for estimating adaptively minor component from input signals. Dynamics of the proposed algorithm are analyzed via a deterministic discrete time (DDT) method. Some sufficient conditions are obtained to guarantee convergence of the proposed algorithm.

Color signals in the Black Grouse (Tetrao tetrix) : signal properties and their condition dependency

Recent progress in techniques of quantifying between‐individual differences of color‐based ornaments has revealed undiscovered possibilities for research in sexual selection. We present how the color spectra data can be comprehensively used for studying the importance of sexual ornaments in the black grouse and how these ornaments are related to a male condition. For this, we used both correlative field and experimental data. Field data indicated that older males had more chromatic coloration than yearlings. Blue chroma of males was correlated with male mating success. We experimentally manipulated yearling birds with testosterone implants and found that testosterone‐implanted males had impaired expression of several sexual ornaments: 10 months after the implantation, both structural‐based blue and carotenoid‐based red eye comb coloration were diminished, as well as lyre (tail) length. However, the manipulation did not affect vital traits under natural selection (wing length or body mass). Our data indicate that structural color is an important trait in sexual selection in this lekking species. Importantly, the data also indicate that male sexual ornaments are more susceptible to environmental conditions than the other male traits, thus showing their heightened condition dependency compared with the other traits mediating the honesty of signaling.

Morphological signals of sex and status in spotted bowerbirds

The Spotted Bowerbird, Chlamydera maculata, appears to be sexually monomorphic. We caught and marked 118 birds in central Queensland, and sexed 88 using molecular methods. We found that our catch was strongly male-biased, both at bower sites and at non-bower feeding sites. We continued to observe the bird's behaviour after their release and so sub-divided males into sexual status groups as either bower-owners or non-owners. We searched for morphological measures, subjectively judged colour differences and quantitatively collected spectral measures of the visual properties of the crest feathers that would allow us to separate birds of differing sex and status. We found that bower owners had larger crests than non-owner males or females and that crest area provided the most accurate predictor of a bird's sex and status in a discriminant function analysis. We studied a cohort of seven males who went from non-owners to bower owners over three years, and found that their change in status was accompanied by a change in crest size – the only significant change in their morphology. Crest size did not relate to the mating success of a bower-owner. Instead, we suggest why the crest may differ between status groups and the implications that this may have for the sexual behaviour of male and female Spotted Bowerbirds.

Maximum contrast analysis for nonnegative blind source separation

In this paper, we propose a maximum contrast analysis (MCA) method for nonnegative blind source separation, where both the mixing matrix and the source signals are nonnegative. We first show that the contrast degree of the source signals is greater than that of the mixed signals. Motivated by this observation, we propose an MCA-based cost function. It is further shown that the separation matrix can be obtained by maximizing the proposed cost function. Then we derive an iterative determinant maximization algorithm for estimating the separation matrix. In the case of two sources, a closed-form solution exists and is derived. Unlike most existing blind source separation methods, the proposed MCA method needs neither the independence assumption, nor the sparseness requirement of the sources. The effectiveness of the new method is illustrated by experiments using X-ray images, remote sensing images, infrared spectral images, and real-world fluorescence microscopy images.

Novel watermarking methods for copyright protection of audio signals

This research presented improved watermarking methods for mono and stereo audio signals. To enhance the performance, novel methods are developed using echo hiding techniques and patchwork-based algorithms. The superior performances of the proposed methods are demonstrated by theoretical analysis and simulation examples, in comparison with the existing methods.

Nonnegative blind source separation by sparse component analysis based on determinant measure

The problem of nonnegative blind source separation (NBSS) is addressed in this paper, where both the sources and the mixing matrix are nonnegative. Because many real-world signals are sparse, we deal with NBSS by sparse component analysis. First, a determinant-based sparseness measure, named D-measure, is introduced to gauge the temporal and spatial sparseness of signals. Based on this measure, a new NBSS model is derived, and an iterative sparseness maximization (ISM) approach is proposed to solve this model. In the ISM approach, the NBSS problem can be cast into row-to-row optimizations with respect to the unmixing matrix, and then the quadratic programming (QP) technique is used to optimize each row. Furthermore, we analyze the source identifiability and the computational complexity of the proposed ISM-QP method. The new method requires relatively weak conditions on the sources and the mixing matrix, has high computational efficiency, and is easy to implement. Simulation results demonstrate the effectiveness of our method.

Nonnegative Blind Source Separation by Sparse Component Analysis Based on Determinant Measure

The problem of nonnegative blind source separation (NBSS) is addressed in this paper, where both the sources and the mixing matrix are nonnegative. Because many real-world signals are sparse, we deal with NBSS by sparse component analysis. First, a determinant-based sparseness measure, named D-measure, is introduced to gauge the temporal and spatial sparseness of signals. Based on this measure, a new NBSS model is derived, and an iterative sparseness maximization (ISM) approach is proposed to solve this model. In the ISM approach, the NBSS problem can be cast into row-to-row optimizations with respect to the unmixing matrix, and then the quadratic programming (QP) technique is used to optimize each row. Furthermore, we analyze the source identifiability and the computational complexity of the proposed ISM-QP method. The new method requires relatively weak conditions on the sources and the mixing matrix, has high computational efficiency, and is easy to implement. Simulation results demonstrate the effectiveness of our method.

Doppler radar in respiratory monitoring : detection and analysis

This paper presents the preliminary results of our work in detecting respiration using Doppler Radar in the 2.7 GHz operating band. We demonstrate the capability of Doppler Radar in capturing breathing patterns under various breathing forms such as normal breathing, fast breathing, as well as different rate of inhale and exhale. From the captured signals, respiration rate was obtained using Fast Fourier Transform and validated. The proposed approach could potentially be used in number of applications involving breathing rate and breathing pattern analysis via non-contact methods.

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management.

Monitoring and analysis of respiratory patterns using microwave doppler radar

 Noncontact detection characteristic of Doppler radar provides an unobtrusive means of respiration detection and monitoring. This avoids additional preparations, such as physical sensor attachment or special clothing, which can be useful for certain healthcare applications. Furthermore, robustness of Doppler radar against environmental factors, such as light, ambient temperature, interference from other signals occupying the same bandwidth, fading effects, reduce environmental constraints and strengthens the possibility of employing Doppler radar in long-term respiration detection, and monitoring applications such as sleep studies. This paper presents an evaluation in the of use of microwave Doppler radar for capturing different dynamics of breathing patterns in addition to the respiration rate. Although finding the respiration rate is essential, identifying abnormal breathing patterns in real-time could be used to gain further insights into respiratory disorders and refine diagnostic procedures. Several known breathing disorders were professionally role played and captured in a real-time laboratory environment using a noncontact Doppler radar to evaluate the feasibility of this noncontact form of measurement in capturing breathing patterns under different conditions associated with certain breathing disorders. In addition to that, inhalation and exhalation flow patterns under different breathing scenarios were investigated to further support the feasibility of Doppler radar to accurately estimate the tidal volume. The results obtained for both experiments were compared with the gold standard measurement schemes, such as respiration belt and spirometry readings, yielding significant correlations with the Doppler radar-based information. In summary, Doppler radar is highlighted as an alternative approach not only for determining respiration rates, but also for identifying breathing patterns and tidal volumes as a preferred nonwearable alternative to the conventional - ontact sensing methods.

Analysis of phasic and tonic electromyographic signal characteristics: electromyographic synthesis and comparison of novel morphological and linear-envelope approaches

The pattern of tonic and phasic components in an EMG signal reflects the underlying behaviour of the central nervous system (CNS) in controlling the musculature. One avenue for gaining a better understanding of this behaviour is to seek a quantitative characterisation of these phasic and tonic components. We propose that these signal characteristics can range between unvarying, tonic and intermittent, phasic activation through a continuum of EMG amplitude modulation. In this paper, we present two new algorithms for quantifying amplitude modulation: a linear-envelope approach, and a mathematical morphology approach. In addition we present an algorithm for synthesising EMG signals with known amplitude modulation. The efficacy of the synthesis algorithm is demonstrated using real EMG data. We present an evaluation and comparison of the two algorithms for quantifying amplitude modulation based on synthetic data generated by the proposed synthesis algorithm. The results demonstrate that the EMG synthesis parameters represent 91.9% and 96.2% of the variance of linear-envelopes extracted from lumbo-pelvic muscle EMG signals collected from subjects performing a repetitive-movement task. This depended, however, on the muscle and movement-speed considered (F=4.02, p<0.001). Coefficients of determination between input and output amplitude modulation variables were used to quantify the accuracy of the linear-envelope and morphological signal processing algorithms. The linear-envelope algorithm exhibited higher coefficients of determination than the most accurate morphological approach (and hence greater accuracy, T=8.16, p<0.001). Similarly, the standard deviation of the coefficients of determination was 1.691 times smaller (p<0.001). This signal processing algorithm represents a novel tool for the quantification of amplitude modulation in continuous EMG signals and can be used in the study of CNS motor control of the musculature in repetitive-movement tasks.

The relative importance of spatial proximity, kin selection and potential ‘greenbeard’ signals on provisioning behaviour among helpers in a cooperative bird

© 2015 Springer-Verlag Berlin Heidelberg Many hypotheses have been proposed to account for cooperative behaviour, with those favouring kin selection receiving the greatest support to date. However, the importance of relatedness becomes less clear in complex societies where interactions can involve both kin and non-kin. To help clarify this, we examined the relative effect of indirect versus key direct benefit hypotheses in shaping cooperative decisions. We assessed the relative importance of likely reciprocal aid (as measured by spatial proximity between participants), kin selection (using molecular-based relatedness indices) and putative signals of relatedness (vocal similarity) on helper/helper cooperative provisioning dynamics in bell miners (Manorina melanophrys), a species living in large, complex societies. Using network analysis, we quantified the extent of shared provisioning (helping at the same nests) among individual helpers (excluding breeding pairs) over three seasons and 4290 provisioning visits, and compared these with the location of individuals within a colony and networks built using either genetic molecular relatedness or call similarity indices. Significant levels of clustering were observed in networks; individuals within a cluster were more closely related to each other than other colony members, and cluster membership was stable across years. The probability of a miner helping at another’s nest was not simply a product of spatial proximity and thus the potential for reciprocal aid. Networks constructed using helping data were significantly correlated to those built using molecular data in 5 of 10 comparisons, compared to 8 of 10 comparisons for networks constructed using call similarity. This suggests an important role of kinship in shaping helping dynamics in a complex cooperative society, apparently determined via an acoustic ‘greenbeard’ signal in this system.

A comparative study of different power system stabilizers for dynamic stability analysis

Power system stabilizers (PSSs) are extensively used to ensure the dynamic stability of power systems through the modulation of excitation signals supplied to synchronous generators. This paper presents a comparative study of two different PSSs: STAB1 and IEEEST. The stabilizers are designed for the linearized model of a single machine infinite bus (SMIB) system with different loads. Both time-and frequency-domain simulations are carried out to investigate the performance of these stabilizers. For all PSSs, the time-domain simulations are performed by applying a three-phase short-circuit fault at the terminal of the synchronous generator. These simulation results are compared against the open-loop characteristics of the SMIB system where no PSS is implemented. Simulation results demonstrate that the speed-fed PSS provides more damping as compared to frequency- and power-fed stabilizers.