An Area-Efficient and Highly Linear Reconfigurable Continuous-Time Filter for Biomedical Sensor Applications.

This paper proposes a compact, high-linearity, and reconfigurable continuous-time filter with a wide frequency-tuning capability for biopotential conditioning. It uses an active filter topology and a new operational-transconductance-amplifier (OTA)-based current-steering (CS) integrator. Consequently, a large time constant τ , good linearity, and linear bandwidth tuning could be achieved in the presented filter with a small silicon area. The proposed filter has a reconfigurable structure that can be operated as a low-pass filter (LPF) or a notch filter (NF) for different purposes. Based on the novel topology, the filter can be readily implemented monolithically and a prototype circuit was fabricated in the 0.18 µm standard complementary-metal-oxide-semiconductor (CMOS) process. It occupied a small area of 0.068 mm2 and consumed 25 µW from a 1.8 V supply. Measurement results show that the cutoff frequency of the LPF could be linearly tuned from 0.05 Hz to 300 Hz and the total-harmonic-distortion (THD) was less than -76 dB for a 2 Hz, 200 mVpp sine input. The input-referred noises were 5.5 µVrms and 6.4 µVrms for the LPF and NF, respectively. A comparison with conventional designs reveals that the proposed design achieved the lowest harmonic distortion and smallest on-chip capacitor. Moreover, its ultra-low cutoff frequency and relatively linear frequency tuning capability make it an attractive solution as an analog front-end for biopotential acquisitions.

A Stochastic Quasi-Newton Method for Large-Scale Nonconvex Optimization With Applications.

Ensuring the positive definiteness and avoiding ill conditioning of the Hessian update in the stochastic Broyden-Fletcher-Goldfarb-Shanno (BFGS) method are significant in solving nonconvex problems. This article proposes a novel stochastic version of a damped and regularized BFGS method for addressing the above problems. While the proposed regularized strategy helps to prevent the BFGS matrix from being close to singularity, the new damped parameter further ensures the positivity of the product of correction pairs. To alleviate the computational cost of the stochastic limited memory BFGS (LBFGS) updates and to improve its robustness, the curvature information is updated using the averaged iterate at spaced intervals. The effectiveness of the proposed method is evaluated through the logistic regression and Bayesian logistic regression problems in machine learning. Numerical experiments are conducted by using both synthetic data set and several real data sets. The results show that the proposed method generally outperforms the stochastic damped LBFGS (SdLBFGS) method. In particular, for problems with small sample sizes, our method has shown superior performance and is capable of mitigating ill-conditioned problems. Furthermore, our method is more robust to the variations of the batch size and memory size than the SdLBFGS method.

Automatic Muscle Fiber Orientation Tracking in Ultrasound Images Using a New Adaptive Fading Bayesian Kalman Smoother.

This paper proposes a new algorithm for automatic estimation of muscle fiber orientation (MFO) in musculoskeletal ultrasound images, which is commonly used for both diagnosis and rehabilitation assessment of patients. The algorithm is based on a novel adaptive fading Bayesian Kalman filter (AF-BKF) and an automatic region of interest (ROI) extraction method. The ROI is first enhanced by the Gabor filter (GF) and extracted automatically using the revoting constrained Radon transform (RCRT) approach. The dominant MFO in the ROI is then detected by the RT and tracked by the proposed AF-BKF, which employs simplified Gaussian mixtures to approximate the non-Gaussian state densities and a new adaptive fading method to update the mixture parameters. An AF-BK smoother (AF-BKS) is also proposed by extending the AF-BKF using the concept of Rauch-Tung-Striebel smoother for further smoothing the fascicle orientations. The experimental results and comparisons show that: 1) the maximum segmentation error of the proposed RCRT is below nine pixels, which is sufficiently small for MFO tracking; 2) the accuracy of MFO gauged by RT in the ROI enhanced by the GF is comparable to that of using multiscale vessel enhancement filter-based method and better than those of local RT and revoting Hough transform approaches; and 3) the proposed AF-BKS algorithm outperforms the other tested approaches and achieves a performance close to those obtained by experienced operators (the overall covariance obtained by the AF-BKS is 3.19, which is rather close to that of the operators, 2.86). It, thus, serves as a valuable tool for automatic estimation of fascicle orientations and possibly for other applications in musculoskeletal ultrasound images.

A Multi-Laplacian Prior and Augmented Lagrangian Approach to the Exploratory Analysis of Time-Varying Gene and Transcriptional Regulatory Networks for Gene Microarray Data.

This paper proposes a novel multi-Laplacian prior (MLP) and augmented Lagrangian method (ALM) approach for gene interactions and putative transcription factors (TFs) identification from time-course gene microarray data. It employs a non-linear time-varying auto-regressive (N-TVAR) model and the Maximum-A-Posteriori-Probability method for incorporating the multi-Laplacian prior and the continuity constraint. The MLP allows connections to/from a gene to be better preserved for putative TF identification in non-stationarity gene regulatory network as compared with conventional L1-based penalties. Moreover, the ALM allows the resultant non-smooth L1-based penalties to be decoupled from the remaining smooth terms, so that the former and latter can be efficiently solved using a low-complexity proximity operator and smooth optimization technique, respectively. Synthetic and real time-course gene microarray datasets are tested to evaluate the performance of the proposed method. Experimental results show that the proposed method gives better accuracy and higher computational speed than our previous work using smoothed approximation. Moreover, its performance, without the use of ChIP-chip data, is found to be highly comparable with other state-of-the-art methods integrating both ChIP-chip and gene microarray data. It suggests that the proposed method may serve as a useful exploratory tool for putative TF identification with reduced experimental cost.

Novel Consensus Gene Selection Criteria for Distributed GPU Partial Least Squares-Based Gene Microarray Analysis in Diffused Large B Cell Lymphoma (DLBCL) and Related Findings.

This paper proposes a novel consensus gene selection criteria for partial least squares-based gene microarray analysis. By quantifying the extent of consistency and distinctiveness of the differential gene expressions across different double cross validations (CV) or randomizations in terms of occurrence and randomization p-values, the proposed criteria are able to identify a more comprehensive genes associated with the underlying disease. A Distributed GPU implementation has been proposed to accelerate the gene selection problem and about 8-11 times speed up has been achieved based on the microarray datasets considered. Simulation results using various cancer gene microarray datasets show that the proposed approach is able to achieve highly comparable classification accuracy in comparing with many conventional approaches. Furthermore, enrichment analysis on the selected genes for Diffused Large B Cell Lymphoma (DLBCL) and Prostate Cancer datasets and show that only the proposed approach is able to identify gene lists enriched in different pathways with significant p-values. In contrast, sufficient statistical significance cannot be found for conventional SVM-RFE and the t-test. The reliability in identifying and establishing statistical significance of the gene findings makes the proposed approach an attractive alternative for cancer related researches based on gene expression profiling or other similar data.

Automatic Extraction of Central Tendon of Rectus Femoris (CT-RF) in Ultrasound Images Using a New Intensity-Compensated Free-Form Deformation-Based Tracking Algorithm With Local Shape Refinement.

Ultrasonography is an important diagnostic imaging technique for visualization of tendons, which provides useful health diagnostic and fundamental information in neuromuscular studies of human motion systems. Conventional ultrasonic-based tendon studies, however, are highly dependent on subjective experience of operators due to various impairments of ultrasound images. Dynamic changes of muscle and tendon deformation in a sequence can hardly be manually processed. Consequently, there is an urgent need for automatic analysis of tendon behavior. This paper proposes an automatic ultrasonic tendon tracking algorithm to extract the shape deformation of central tendon of rectus femoris (CT-RF) from ultrasonic image sequences. The tracking problem is complicated by the highly deformable tendon, time-varying brightness, and the inconspicuousness of the target. To address this difficult tracking problem, we proposed a new intensity-compensated free-form deformation (IC-FFD)-based tracking algorithm with local shape refinement (LSR). Experimental results and comparison show that the proposed IC-FFD-LSR algorithm outperforms IC-FFD and conventional methods such as MI-FFD in CT-RF tracking.

Task-related functional connectivity dynamics in a block-designed visual experiment.

Studying task modulations of brain connectivity using functional magnetic resonance imaging (fMRI) is critical to understand brain functions that support cognitive and affective processes. Existing methods such as psychophysiological interaction (PPI) and dynamic causal modeling (DCM) usually implicitly assume that the connectivity patterns are stable over a block-designed task with identical stimuli. However, this assumption lacks empirical verification on high-temporal resolution fMRI data with reliable data-driven analysis methods. The present study performed a detailed examination of dynamic changes of functional connectivity (FC) in a simple block-designed visual checkerboard experiment with a sub-second sampling rate (TR = 0.645 s) by estimating time-varying correlation coefficient (TVCC) between BOLD responses of different brain regions. We observed reliable task-related FC changes (i.e., FCs were transiently decreased after task onset and went back to the baseline afterward) among several visual regions of the bilateral middle occipital gyrus (MOG) and the bilateral fusiform gyrus (FuG). Importantly, only the FCs between higher visual regions (MOG) and lower visual regions (FuG) exhibited such dynamic patterns. The results suggested that simply assuming a sustained FC during a task block may be insufficient to capture distinct task-related FC changes. The investigation of FC dynamics in tasks could improve our understanding of condition shifts and the coordination between different activated brain regions.

A Maximum A Posteriori Probability and Time-Varying Approach for Inferring Gene Regulatory Networks from Time Course Gene Microarray Data.

Unlike most conventional techniques with static model assumption, this paper aims to estimate the time-varying model parameters and identify significant genes involved at different timepoints from time course gene microarray data. We first formulate the parameter identification problem as a new maximum a posteriori probability estimation problem so that prior information can be incorporated as regularization terms to reduce the large estimation variance of the high dimensional estimation problem. Under this framework, sparsity and temporal consistency of the model parameters are imposed using L1-regularization and novel continuity constraints, respectively. The resulting problem is solved using the L-BFGS method with the initial guess obtained from the partial least squares method. A novel forward validation measure is also proposed for the selection of regularization parameters, based on both forward and current prediction errors. The proposed method is evaluated using a synthetic benchmark testing data and a publicly available yeast Saccharomyces cerevisiae cell cycle microarray data. For the latter particularly, a number of significant genes identified at different timepoints are found to be biological significant according to previous findings in biological experiments. These suggest that the proposed approach may serve as a valuable tool for inferring time-varying gene regulatory networks in biological studies.

Adaptive covariance estimation of non-stationary processes and its application to infer dynamic connectivity from fMRI.

Time-varying covariance is an important metric to measure the statistical dependence between non-stationary biological processes. Time-varying covariance is conventionally estimated from short-time data segments within a window having a certain bandwidth, but it is difficult to choose an appropriate bandwidth to estimate covariance with different degrees of non-stationarity. This paper introduces a local polynomial regression (LPR) method to estimate time-varying covariance and performs an asymptotic analysis of the LPR covariance estimator to show that both the estimation bias and variance are functions of the bandwidth and there exists an optimal bandwidth to minimize the mean square error (MSE) locally. A data-driven variable bandwidth selection method, namely the intersection of confidence intervals (ICI), is adopted in LPR for adaptively determining the local optimal bandwidth that minimizes the MSE. Experimental results on simulated signals show that the LPR-ICI method can achieve robust and reliable performance in estimating time-varying covariance with different degrees of variations and under different noise scenarios, making it a powerful tool to study the dynamic relationship between non-stationary biomedical signals. Further, we apply the LPR-ICI method to estimate time-varying covariance of functional magnetic resonance imaging (fMRI) signals in a visual task for the inference of dynamic functional brain connectivity. The results show that the LPR-ICI method can effectively capture the transient connectivity patterns from fMRI.

Time-varying correlation coefficients estimation and its application to dynamic connectivity analysis of fMRI.

Exploration of the dynamics of functional brain connectivity based on the correlation coefficients of functional magnetic resonance imaging (fMRI) data is important for understanding the brain mechanisms. Because fMRI data are time-varying in nature, the functional connectivity shows substantial fluctuations and dynamic characteristics. However, an effective method for estimating time-varying functional connectivity is lacking, which is mainly due to the difficulty in choosing an appropriate window to localize the time-varying correlation coefficients (TVCC). This paper introduces a novel method for adaptively estimating the TVCC of non-stationary signals and studies its application to infer dynamic functional connectivity of fMRI data in a visual task. The proposed method employs a sliding window having a certain bandwidth to estimate the TVCC locally and the window bandwidths are selected adaptively by a local plug-in rule to minimize the mean squared error. The results show that the functional connectivity changes in the visual task are transient, which suggests that simply assuming sustained connectivity changes during task period might not be sufficient to capture dynamic connectivity changes induced by tasks.

Sonomyographic responses during voluntary isometric ramp contraction of the human rectus femoris muscle.

This paper aims to investigate the relationship between torque and muscle morphological change, which is derived from ultrasound image sequence and termed as sonomyography (SMG), during isometric ramp contraction of the rectus femoris (RF) muscle, and to further compare SMG with the electromyography (EMG) and mechanomyography (MMG), which represent the electrical and mechanical activities of the muscle. Nine subjects performed isometric ramp contraction of knee up to 90% of the maximal voluntary contraction (MVC) at speeds of 45, 22.5 and 15% MVC/s, and EMG, MMG and ultrasonography were simultaneously recorded from the RF muscle. Cross-sectional area, which was referred to as SMG, was automatically extracted from continuously captured ultrasound images using a newly developed image tracking algorithm. Polynomial regression analyses were applied to fit the EMG/MMG/SMG-to-torque relationships, and the regression coefficients of EMG, MMG, and SMG were compared. Moreover, the effect of contraction speed on SMG/EMG/MMG-to-torque relationships was tested by pair-wise comparisons of the mean relationship curves at different speeds for EMG, MMG and SMG. The results show that continuous SMG could provide important morphological parameters of continuous muscle contraction. Compared with EMG and MMG, SMG exhibits different changing patterns with the increase of torque during voluntary isometric ramp contraction, and it is less influenced by the contraction speed.

Time-frequency component analysis of somatosensory evoked potentials in rats.

BACKGROUND: Somatosensory evoked potential (SEP) signal usually contains a set of detailed temporal components measured and identified in a time domain, giving meaningful information on physiological mechanisms of the nervous system. The purpose of this study is to measure and identify detailed time-frequency components in normal SEP using time-frequency analysis (TFA) methods and to obtain their distribution pattern in the time-frequency domain. METHODS: This paper proposes to apply a high-resolution time-frequency analysis algorithm, the matching pursuit (MP), to extract detailed time-frequency components of SEP signals. The MP algorithm decomposes a SEP signal into a number of elementary time-frequency components and provides a time-frequency parameter description of the components. A clustering by estimation of the probability density function in parameter space is followed to identify stable SEP time-frequency components. RESULTS: Experimental results on cortical SEP signals of 28 mature rats show that a series of stable SEP time-frequency components can be identified using the MP decomposition algorithm. Based on the statistical properties of the component parameters, an approximated distribution of these components in time-frequency domain is suggested to describe the complex SEP response. CONCLUSION: This study shows that there is a set of stable and minute time-frequency components in SEP signals, which are revealed by the MP decomposition and clustering. These stable SEP components have specific localizations in the time-frequency domain.