EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery

Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. Mean entropy of MSE is used as an index to find artifacts-free intrinsic mode functions. The effect of different levels of artifacts on the performances of the proposed filtering is analysed using simulated data. Furthermore, 21 patients' EEG signals are collected and analysed using sample entropy to calculate the complexity for monitoring DOA. The correlation coefficients of entropy and bispectral index (BIS) results show 0.14 ± 0.30 and 0.63 ± 0.09 before and after filtering, respectively. Artificial neural network (ANN) model is used for range mapping in order to correlate the measurements with BIS. The ANN method results show strong correlation coefficient (0.75 ± 0.08). The results in this paper verify that entropy values and BIS have a strong correlation for the purpose of DOA monitoring and the proposed filtering method can effectively filter artifacts from EEG signals. The proposed method performs better than the commonly used wavelet denoising method. This study provides a fully adaptive and automated filter for EEG to measure DOA more accuracy and thus reduce risk related to maintenance of anaesthetic agents.This research was financially supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant Number: NSC102-2911-I-008-001). Also, it was supported by Chung-Shan Institute of Science and Technology in Taiwan (Grant Numbers: CSIST-095-V301 and CSIST-095-V302) and National Natural Science Foundation of China (Grant Number: 51475342)

A novel low complexity cluster based MLSE equalizer for QPSK signaling scheme

In this paper, a novel Maximum Likelihood Sequence Estimation (MLSE) equalizer is presented. The method does not require the explicit modeling of the channel and it belongs to the class of Cluster Based Sequence Equalizers (CBSE). The novelty of the method is that the required clusters of the received data are estimated in the one dimensional space via a technique that exploits the underlying symmetries in the structure of the received clusters. This gives a two-fold advantage. The computational load is drastically reduced, and at the same time the convergence speed is improved compared to both LMS based channel estimators and previously suggested cluster based equalizers

A novel cluster based MLSE equalizer for M -PAM signaling schemes

In this paper a new cluster based Maximum Likelihood Sequence Equalizer is presented. The novelty of the algorithm consists of a new technique for the estimation of all the centers around which the received observations are clustered. For a channel of order L and M-PAM signaling scheme, only L of the cluster centers need to be estimated, and the rest, ML-L, are subsequently computed via simple operations. This has a two-fold advantage compared to previously proposed cluster based algorithms. It reduces dramatically both the computational complexity and the required length of the training sequence. The new method is compared with the standard LMS and RLS based MLSE and the Bayesian RBF equalizer. Moreover, the overmodeling and the undermodeling cases are also explored. The results are very favorable for the new technique, from the computational as well as the performance point of view. © 2003 Elsevier B.V. All rights reserved

An Efficient Low-Complexity Technique for MLSE Equalizers for Linear and Nonlinear Channels

In this paper, a novel sequence equalizer, which belongs to the family of cluster-based sequence equalizers, is presented. The proposed algorithm achieves the maximum likelihood solution to the equalization problem in a fraction of computational load, compared with the classic maximum likelihood sequence estimation (MLSE) equalizers. The new method does not require the estimation of the channel impulse response. Instead, it utilizes the estimates of the cluster centers formed by the received observations. Furthermore, a new cluster center estimation scheme, which exploits the intrinsic dependencies among the cluster centers, is proposed. The new center estimation method exhibits enhanced performance with respect to convergence speed, compared with an LMS-based channel estimator. Moreover, this gain in performance is obtained at substantially lower computational load. The method is also extended in order to cope with nonlinear channels. The performance of the new equalizer is tested with several simulation examples, using both the quadrature phase shift keying (QPSK) and the 16-quadrature amplitude modulated (QAM) signaling schemes for linear and nonlinear communication channels

Robust Subspace Tracking with Missing Entries: The Set-Theoretic Approach

In this paper, an Adaptive Projected Subgradient Method (APSM) based algorithm for robust subspace tracking is introduced. A properly chosen cost function is constructed at each time instance and the goal is to seek for points, which belong to the zero level set of this function; i.e., the set of points which score a zero loss. At each iteration, an outlier detection mechanism is employed, in order to conclude whether the current data vector contains outlier noise or not. In the sequel, a sparsity-promoting greedy algorithm is employed for the outlier vector estimation allowing the purification of the corrupted data from the outlier noise, prior to any further processing. Furthermore, the case where the observation vectors are partially observed is attacked via a prediction procedure, which estimates the values of the unobserved (missing) coefficients. A theoretical analysis is carried out and the simulation experiments, within the contexts of robust subspace estimation and robust matrix completion, demonstrate the enhanced performance of the proposed scheme compared to recently developed state of the art algorithms. © 1991-2012 IEEE

Online sparse system identification and signal reconstruction using projections onto weighted ℓ1 balls

This paper presents a novel projection-based adaptive algorithm for sparse signal and system identification. The sequentially observed data are used to generate an equivalent sequence of closed convex sets, namely hyperslabs. Each hyperslab is the geometric equivalent of a cost criterion, that quantifies "data mismatch". Sparsity is imposed by the introduction of appropriately designed weighted ℓ1 balls and the related projection operator is also derived. The algorithm develops around projections onto the sequence of the generated hyperslabs as well as the weighted ℓ1 balls. The resulting scheme exhibits linear dependence, with respect to the unknown system's order, on the number of multiplications/ additions and an Ο(Llog2L) dependence on sorting operations, where $L$ is the length of the system/signal to be estimated. Numerical results are also given to validate the performance of the proposed method against the Least-Absolute Shrinkage and Selection Operator (LASSO) algorithm and two very recently developed adaptive sparse schemes that fuse arguments from the LMS/RLS adaptation mechanisms with those imposed by the lasso rational. © 2010 IEEE

Unsupervised pre-training of the brain connectivity dynamic using residual D-Net

In this paper, we propose a novel unsupervised pre-training method to learn the brain dynamics using a deep learning architecture named residual D-net. As it is often the case in medical research, in contrast to typical deep learning tasks, the size of the resting-state functional Magnetic Resonance Image (rs-fMRI) datasets for training is limited. Thus, the available data should be very efficiently used to learn the complex patterns underneath the brain connectivity dynamics. To address this issue, we use residual connections to alleviate the training complexity through recurrent multi-scale representation and pre-training the architecture unsupervised way. We conduct two classification tasks to differentiate early and late stage Mild Cognitive Impairment (MCI) from Normal healthy Control (NC) subjects. The experiments verify that our proposed residual D-net indeed learns the brain connectivity dynamics, leading to significantly higher classification accuracy compared to previously published techniques. © Springer Nature Switzerland AG 2019

Instantaneous frequency based spectral analysis of nuclear magnetic resonance spectroscopy data

Nuclear magnetic resonance spectroscopy signals are modelled as a sum of decaying complex exponentials in noise. The spectral analysis of these signals allowing for their decomposition and the estimation of the parameters of the components is crucial to the study of biochemical samples. This paper presents a novel Gabor filterbank/notch filtering instantaneous frequency (IF) estimator, that enables the extraction of weaker and shorter lived exponentials. This new approach is an iterative procedure where a Gabor filterbank is first employed to obtain a reliable estimate of the IF of the strongest component present. The estimated strongest component is then notch filtered, which un-masks weaker components, and the procedure repeated. The performance of this method was evaluated using an artificial signal and compared to the short time Fourier transform, reassigned STFT, and the original Gabor filterbank approach. The results clearly demonstrate its superiority in uncovering weaker signals and resolving components that are very close to one another in frequency. Furthermore, the new method is shown to be more robust than the ITCMP technique at low signal to noise ratios. © 2011 Elsevier Ltd. All rights reserved

On the least-squares performance of a novel efficient center estimation method for clustering-based MLSE equalization

Recently, a novel maximum-likelihood sequence estimation (MLSE) equalizer was reported that avoids the explicit estimation of the channel impulse response. Instead, it is based on the fact that the (noise-free) channel outputs, needed by the Viterbi algorithm, coincide with the points around which the received (noisy) samples are clustered and can thus be estimated directly with the aid of a supervised clustering method. Moreover, this is achieved in a computationally efficient manner that exploits the channel linearity and the symmetries underlying the transmitted signal constellation. The resulting computational savings over the conventional MLSE equalization scheme are significant even in the case of relatively short channels where MLSE equalization is practically applicable. It was demonstrated, via simulations, that the performance of this algorithm is close to that using a least-squares (LS) channel estimator, although its computational complexity is even lower than that of the least-mean squares (LMS)-trained MLSE equalizer. This paper investigates the relationship of the center estimation (CE) part of the proposed equalizer with the LS method. It is proved that, when using LS with the training sequence employed by CE, the two methods lead to the same solution. However, when LS is trained with random data, it outperforms CE, with the performance difference being proportional to the channel length. A modified CE method, called MCE, is thus developed, that attains the performance of LS with perfectly random data, while still being much simpler computationally than classical LS estimation. Through the results of this paper, CE is confirmed as a methodology that combines high performance, simplicity, and low computational cost, as required in a practical equalization task. An alternative, algebraic viewpoint on the CE method is also provided. © 2006 IEEE