Selin Aviyente

Evidence of disrupted functional connectivity in the brain after combat-related blast injury

Non-impact blast-related mild traumatic brain injury (mTBI) appears to be present in soldiers returning from deployments to Afghanistan and Iraq. Although mTBI typically results in cognitive deficits that last less than a month, there is evidence that disrupted coordination of brain activity can persist for at least several months following injury (Thatcher et al., 1989, 2001). In the present study we examined whether neural communication may be affected in soldiers months after blast-related mTBI, and whether coordination of neural function is associated with underlying white matter integrity. The investigation included an application of a new time-frequency based method for measuring electroencephalogram (EEG) phase synchronization (Aviyente et al., 2010) as well as fractional anisotropy measures of axonal tracts derived from diffusion tensor imaging (DTI). Nine soldiers who incurred a blast-related mTBI during deployments to Afghanistan or Iraq were compared with eight demographically similar control subjects. Despite an absence of cognitive deficits, the blast-related mTBI group exhibited diminished EEG phase synchrony of lateral frontal sites with contralateral frontal brain regions suggesting diminished interhemispheric coordination of brain activity as a result of blast injury. For blast injured (i.e., blast-related mTBI) soldiers we found that EEG phase synchrony was associated with the structural integrity of white matter tracts of the frontal lobe (left anterior thalamic radiations and the forceps minor including the anterior corpus callosum). Analyses revealed that diminished EEG phase synchrony was not the consequence of combat-stress symptoms (e.g., post-traumatic stress and depression) and commonly prescribed medications. Results provide evidence for poor coordination of frontal neural function after blast injury that may be the consequence of damaged anterior white matter tracts.

Compressed Sensing Framework for EEG Compression

Many applications in signal processing require the efficient representation and processing of data. The traditional approach to efficient signal representation is compression. In recent years, there has been a new approach to compression at the sensing level. Compressed sensing (CS) is an emerging field which is based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper, we propose an application of compressed sensing in the field of biomedical signal processing, particularly electroencophelogram (EEG) collection and storage. A compressed sensing framework is introduced for efficient representation of multichannel, multiple trial EEG data. The proposed framework is based on the revelation that EEG signals are sparse in a Gabor frame. The sparsity of EEG signals in a Gabor frame is utilized for compressed sensing of these signals. A simultaneous orthogonal matching pursuit algorithm is shown to be effective in the joint recovery of the original multiple trail EEG signals from a small number of projections.

Wavelet Feature Selection for Image Classification

Energy distribution over wavelet subbands is a widely used feature for wavelet packet based texture classification. Due to the overcomplete nature of the wavelet packet decomposition, feature selection is usually applied for a better classification accuracy and a compact feature representation. The majority of wavelet feature selection algorithms conduct feature selection based on the evaluation of each subband separately, which implicitly assumes that the wavelet features from different subbands are independent. In this paper, the dependence between features from different subbands is investigated theoretically and simulated for a given image model. Based on the analysis and simulation, a wavelet feature selection algorithm based on statistical dependence is proposed. This algorithm is further improved by combining the dependence between wavelet feature and the evaluation of individual feature component. Experimental results show the effectiveness of the proposed algorithms in incorporating dependence into wavelet feature selection.

Identification of Intermittent Electrical and Mechanical Faults in Permanent-Magnet AC Drives Based on Time–Frequency Analysis

The detection of noncatastrophic faults in conjunction with other factors can be used to determine the remaining life of an electric drive. As the frequency and severity of these faults increase, the working life of the drive decreases, leading to eventual failure. In this paper, methods are presented to identify developing electrical and mechanical faults based on both the short-time Fourier transform and wavelet analysis of the field-oriented currents in permanent-magnet ac drives. The different fault types are classified by developing a linear discriminant classifier based on the transform coefficients.

Time–Frequency Analysis for Efficient Fault Diagnosis and Failure Prognosis for Interior Permanent-Magnet AC Motors

The detection of noncatastrophic faults in conjunction with other factors can be used to determine the remaining life of an electric drive. As the frequency and severity of these faults increase, the working life of the drive decreases, leading to eventual failure. In this paper, four methods to identify developing electrical faults are presented and compared. They are based on the short-time Fourier transform, undecimated-wavelet analysis, and Wigner and Choi-Williams distributions of the field-oriented currents in permanent-magnet ac drives. The different fault types are classified using the linear-discriminant classifier and k-means classification. The comparison between the different methods is based on the number of correct classifications and Fishers discriminant ratio. Multiple-class discrimination analysis is also introduced to remove redundant information and minimize storage requirements.

A phase synchrony measure for quantifying dynamic functional integration in the brain

The temporal coordination of neural activity within structural networks of the brain has been posited as a basis for cognition. Changes in the frequency and similarity of oscillating electrical potentials emitted by neuronal populations may reflect the means by which networks of the brain carry out functions critical for adaptive behavior. A computation of the phase relationship between signals recorded from separable brain regions is a method for characterizing the temporal interactions of neuronal populations. Recently, different phase estimation methods for quantifying the time‐varying and frequency‐dependent nature of neural synchronization have been proposed. The most common method for measuring the synchronization of signals through phase computations uses complex wavelet transforms of neural signals to estimate their instantaneous phase difference and locking. In this article, we extend this idea by introducing a new time‐varying phase synchrony measure based on Cohens class of time–frequency distributions. This index offers improvements over existing synchrony measures by characterizing the similarity of signals from separable brain regions with uniformly high resolution across time and frequency. The proposed measure is applied to both synthesized signals and electroencephalography data to test its effectiveness in estimating phase changes and quantifying neural synchrony in the brain. Hum Brain Mapp, 2010.

Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings

Condition-based maintenance, which includes both diagnosis and prognosis of faults, is a topic of growing interest for improving the reliability of electrical drives. Bearings constitute a large portion of failures in rotational machines. Although many techniques have been successfully applied for bearing fault diagnosis, prognosis of faults, particularly predicting the remaining useful life (RUL) of bearings, is a remaining challenge. The main reasons for this are a lack of accurate physical degradation models and limited labeled training data. In this paper, we introduce a data-driven methodology, which relies on both time and time-frequency domain features to track the evolution of bearing faults. Once features are extracted, an analytical function that best approximates the evolution of the fault is determined and used to learn the parameters of an extended Kalman filter (KF). The learned extended KF is applied to testing data to predict the RUL of bearing faults under different operating conditions. The performance of the proposed method is evaluated on PRONOSTIA experimental testbed data.

Prognosis of Gear Failures in DC Starter Motors Using Hidden Markov Models

Diagnosis classifies the present state of operation of the equipment, and prognosis predicts the next state of operation and its remaining useful life. In this paper, a prognosis method for the gear faults in dc machines is presented. The proposed method uses the time-frequency features extracted from the motor current as machine health indicators and predicts the future state of fault severity using hidden Markov models (HMMs). Parameter training of HMMs generally needs huge historical data, which are often not available in the case of electrical machines. Methods for computing the parameters from limited data are presented. The proposed prognosis method uses matching pursuit decomposition for estimating state-transition probabilities and experimental observations for computing state-dependent observation probability distributions. The proposed method is illustrated by examples using data collected from the experimental setup.

Minimum entropy time-frequency distributions

Re/spl acute/nyi entropy has been proposed as an effective measure of signal information content and complexity on the time-frequency plane. The previous work concerning Re/spl acute/nyi entropy in the time-frequency plane has focused on measuring the complexity of a given deterministic signal. In this paper, the properties of Re/spl acute/nyi entropy for random signals are examined. The upper and lower bounds on the expected value of Re/spl acute/nyi entropy are derived and ways of minimizing the entropy of time-frequency distributions by putting constraints on the time-frequency kernel are explored. It is proven that the quasi-Wigner kernel has the minimum entropy among all positive time-frequency kernels with finite time-support and correct marginals. A general class of minimum entropy kernels is presented. The performance of minimum entropy kernels in signal representation and component counting is also demonstrated.

Scale Invariant Feature Extraction Algorithm for the Automatic Diagnosis of Rotor Asymmetries in Induction Motors

The development of portable devices that make the reliable diagnosis of faults in electric motors possible has become a challenge for many researchers and maintenance enterprises. These machines intervene in a huge amount of processes and applications and their eventual failure may imply important costs in terms of time and money. However, the aforementioned issue remains unsolved because most of the developed fault diagnosis techniques rely on the user expertise, since they are based on a qualitative interpretation of the results. This complicates the implementation of these methodologies in condition monitoring systems or devices. The objective of this paper is to propose an integral methodology that is able to diagnose the presence of rotor bar failures in an automatic way. The proposed algorithm combines the Discrete Wavelet Transform with the scale transform for feature extraction and correlation coefficient for pattern recognition. The algorithm is applied to both small and large motors operating in a wide range of conditions. The results illustrate the validity and generality of the approach for automatic condition monitoring of electric motors.