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Closed-loop control plays an important role in the treatment of epileptiform spikes by using brain stimulation. In recent years, there have been many analytical methods for determining stimulus protocols and stimulus parameters. However, the analytical method that can start the stimulus protocol when it is needed and stop the stimulus protocol when it is not needed is rather rare. In this work, we design an analytic closed-loop control scheme which can starts control when epileptiform spikes are detected and stops control when no epileptiform spikes are detected. The neural mass model is used to simulate the generation of normal Electroencephalograph signals and epileptiform spikes. The detection of epileptiform spikes is completed via an alarm threshold which is set by using the combination of cross approximate entropy, the Pearson correlation coefficient and the fuzzy theory. If the detection result shows that there are epileptiform spikes in the neural mass model, the fuzzy proportion integration differentiation control works so that the abnormal epileptiform spikes are restored to normal EEG signals, and vice versa. The simulation confirms the effectiveness of the proposed closed-loop control scheme.

Aiming at the fault diagnosis problem of rolling bearing,a fault diagnosis method of rolling bearing based on local characteristic-scale decomposition（LCD） cross approximate entropy（CAE） and relevance vector machine（RVM） is proposed. Firstly,the bearing vibration signals is decomposed into several intrinsic scale components（ISC） which with different frequency components. Secondly,some ISCs that contain main fault information are shifted out by the energy analysis criterion and CAE values are calculated as fault feature vectors that could represent the operating conditions of bearings. Finally,the fault feature are put into RVM to identify different faults. The effective of the proposed method is verified by the different fault type and different fault degree of rolling bearing experiment.

Cross-Approximate Entropy (Cross-ApEn) is a useful measure to quantify the statistical dissimilarity of two time series. In spite of the advantage of Cross-ApEn over its one-dimensional counterpart (Approximate Entropy), only a few studies have applied it to biomedical signals, mainly due to its high computational cost. In this paper, we propose a fast GPU-based implementation of the Cross-ApEn that makes feasible its use over a large amount of multidimensional data. The scheme followed is fully scalable, thus maximizes the use of the GPU despite of the number of neural signals being processed. The approach consists in processing many trials or epochs simultaneously, with independence of its origin. In the case of MEG data, these trials can proceed from different input channels or subjects. The proposed implementation achieves an average speedup greater than 250× against a CPU parallel version running on a processor containing six cores. A dataset of 30 subjects containing 148 MEG channels (49 epochs of 1024 samples per channel) can be analyzed using our development in about 30min. The same processing takes 5 days on six cores and 15 days when running on a single core. The speedup is much larger if compared to a basic sequential Matlab® implementation, that would need 58 days per subject. To our knowledge, this is the first contribution of Cross-ApEn measure computation using GPUs. This study demonstrates that this hardware is, to the day, the best option for the signal processing of biomedical data with Cross-ApEn. [ABSTRACT FROM AUTHOR]

A neuro-fuzzy based model is proposed in this paper for estimating the Lyapunov exponents of an unknown dynamical system according solely to a set of observations. Several approaches have been presented in recent years; most of them using the approximation of both the function of the trajectory of observations and the partial derivatives, to yield the Jacobian matrix of the function. The Jacobian matrix is then employed in the Jacobian-based methods that extract the Lyapunov exponents by QR-decomposition. While the accurate estimation of Lyapunov exponents has been sought, most of the related papers mainly focus on the accuracy of the trajectory approximation. In this paper, an Adaptive Neuro-Fuzzy Inference System is presented and stated to be an efficient tool for such a purpose. Structural parameters of the proposed model as the embedding dimension and the delay time are calculated by the Takens theorem and autocorrelation function, respectively. Model validation is performed by cross approximate entropy. Then, the promising performance of the proposed model as an accurate estimation of the Lyapunov exponents and its robustness to the measurement noise are finally evaluated. [ABSTRACT FROM AUTHOR]

A complexity estimating algorithm, cross approximate entropy (Cross-ApEn), was developed to analyze the instantaneous synchronization of cognitive ERPs, which could indicate the interactions of different brain regions during selective attention tasks. In order to illustrate the validity of Cross-ApEn' s reflection of instantaneous synchronization, cross covariance was also calculated to compare with the final result. Two main phenomena can be noticed from this investigation: (1) The Cross-ApEn of electrode pairs over frontal and parietal areas became lower during the response period (0–400 ms) in both incongruent and congruent situations. (2) It was shown that Cross-ApEn presents lower value in incongruent situation than that in congruent one during response period (0–400 ms) at most electrode pairs. These results demonstrated that Cross-ApEn is effective to evaluate ERPs' instantaneous synchronization designating the cooperation of different brain regions and may bring a new view to cognitive neural imaging and recognition. [ABSTRACT FROM AUTHOR]