Autoregressive Modeling Technique Research Articles

Ictal propagation of high frequency activity is recapitulated in interictal recordings: Effective connectivity of epileptogenic networks recorded with intracranial EEG

Seizures are increasingly understood to arise from epileptogenic networks across which ictal activity is propagated and sustained. In patients undergoing invasive monitoring for epilepsy surgery, high frequency oscillations have been observed within the seizure onset zone during both ictal and interictal intervals. We hypothesized that the patterns by which high frequency activity is propagated would help elucidate epileptogenic networks and thereby identify network nodes relevant for surgical planning. Intracranial EEG recordings were analyzed with a multivariate autoregressive modeling technique (short-time direct directed transfer function—SdDTF), based on the concept of Granger causality, to estimate the directionality and intensity of propagation of high frequency activity (70–175Hz) during ictal and interictal recordings. These analyses revealed prominent divergence and convergence of high frequency activity propagation at sites identified by epileptologists as part of the ictal onset zone. In contrast, relatively little propagation of this activity was observed among the other analyzed sites. This pattern was observed in both subdural and depth electrode recordings of patients with focal ictal onset, but not in patients with a widely distributed ictal onset. In patients with focal ictal onsets, the patterns of propagation recorded during pre-ictal (up to 5min immediately preceding ictal onset) and interictal (more than 24h before and after seizures) intervals were very similar to those recorded during seizures. The ability to characterize epileptogenic networks from interictal recordings could have important clinical implications for epilepsy surgery planning by reducing the need for prolonged invasive monitoring to record spontaneous seizures.

AR Model Based Human Identification using Ear Biometrics

In this paper usefulness of time series based Auto Regressive (AR) modelling technique has been explored for identification of a person. For this purpose, time series is obtained from the contour coordinates of the ear. AR model is fitted to this time series. AR coefficients thus obtained serve as a feature vector. Recognition Rate (RR) has been found by a classifier that is based on Euclidian distance between feature vector of test samples with training samples within itself (intraclass) and with respect to others (interclass). Model has been found invariant to posture, rotation and illumination. RR up to 99% has been obtained. Results have been compared with existing techniques. The results demonstrate the effectiveness of technique for human identification.

Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification

Brain connectivity network derived from functional magnetic resonance imaging (fMRI) is becoming increasingly prevalent in the researches related to cognitive and perceptual processes. The capability to detect causal or effective connectivity is highly desirable for understanding the cooperative nature of brain network, particularly when the ultimate goal is to obtain good performance of control-patient classification with biological meaningful interpretations. Understanding directed functional interactions between brain regions via brain connectivity network is a challenging task. Since many genetic and biomedical networks are intrinsically sparse, incorporating sparsity property into connectivity modeling can make the derived models more biologically plausible. Accordingly, we propose an effective connectivity modeling of resting-state fMRI data based on the multivariate autoregressive (MAR) modeling technique, which is widely used to characterize temporal information of dynamic systems. This MAR modeling technique allows for the identification of effective connectivity using the Granger causality concept and reducing the spurious causality connectivity in assessment of directed functional interaction from fMRI data. A forward orthogonal least squares (OLS) regression algorithm is further used to construct a sparse MAR model. By applying the proposed modeling to mild cognitive impairment (MCI) classification, we identify several most discriminative regions, including middle cingulate gyrus, posterior cingulate gyrus, lingual gyrus and caudate regions, in line with results reported in previous findings. A relatively high classification accuracy of 91.89 % is also achieved, with an increment of 5.4 % compared to the fully-connected, non-directional Pearson-correlation-based functional connectivity approach.

Exchange Rate Pass Through in a Small Open Economy: A case study of West African Monetary Zone

The study investigated the magnitude of exchange rate pass through to import prices and domestic prices   (consumer price index) in WAMZ economy using quarterly time-series data between 2000 and 2010 with the aids of Vector autoregressive (VAR) modeling technique supported with Johansen co-integration approach cross country analysis comprising of Gambia, Ghana, Nigeria and Sierra-Leone. The study discovered that transmission of exchange rate to import prices is more when compared with consumer price in the zone while the contributions of exchange rate to import price are not less 13 percent at average in entire zone. Consumer price index was explained by exchange rate pass through with an average of 26 percent in the zone where the pass through to consumer price is less than two percent in Ghanaian economy. The Taylor (2000) hypothesis was observed in the study where Ghana and Nigeria are the outlier economies while Nigeria established a positive relationship between interest rate volatility and exchange rate pass through to import prices.

Model for simulating scorpion substrate vibration and detection system

Scorpion stings are vital health issues which requires prompt attention to minimize the pain inflicted on victims and avert death. A possible solution in averting the sting is the capability of detecting its presence earlier before it stings. Scorpion like other arthropods have a specific kind of movement pattern called substrate vibration, which generates a specific signal that is used in recognizing and locating mates and preys. This paper aims at developing an intelligent scorpion detection system using vibration frequency detection technique. A six step model for simulating scorpion substrate vibration and detection has been proposed. The surrounding vibrating signal is acquired and passed through a band pass filter. The resulting signal is model using autoregressive modeling technique. Resulting co-efficients are further analyzed for activity detection. The frequency response of scorpion activities for mating behaviour was simulated, detected analysed using MATLAB environment. The resulting coefficients was also compared and analysed. Results obtained shows that the proposed technique is appropriate for model and simulating scorpion substrate vibration and detection system.

Distribution of transfers and soft budget spending behaviors: evidence from Italian regions

This paper examines empirically the strategic interactions among central and subcentral governments when incomplete information forces the subcentral government to form expectations about the amount of transfers that it will receive from the central one. The empirical analysis features a wide array of proxies for transfer expectations and uses an autoregressive modeling technique to estimate them on a sample of 20 Italian Regions for the 1995–2009 time interval. The analysis shows that transfer expectations are a quantitatively important component of local government spending, revealing evidence of soft budget spending behavior.

Forecasting of monthly river flow with autoregressive modeling and data-driven techniques

This study was conducted by using autoregressive (AR) modeling and data-driven techniques which include gene expression programming (GEP), radial basis function network and feed-forward neural netw...

A Time-Domain Model of Background Noise for In-Home MIMO PLC Networks

Multiple-input multiple-output techniques have recently become an important research field for enhancing the performance of in-home power-line communication (PLC) systems by exploiting the additional protective earth wire. The development of such systems requires an accurate description of the channel noise. In this paper, we have presented a model for PLC background noise based on an extensive set of measurements. We have adopted the framework of the multivariate time series to model the PLC background noise. This paper employs the vector autoregressive modeling technique to extract noise model parameters from the measured noise. We have verified the accuracy of the noise model by comparing time- and frequency-domain correlation of measured and modeled noise.

Artificial neural network based autoregressive modeling technique with application in voice activity detection

A new method of estimating the coefficients of an autoregressive (AR) model using real-valued neural network (RVNN) technique is presented in this paper. The coefficients of the AR model are obtained from the synaptic weights and adaptive coefficients of the activation function of a two layer RVNN while the number of neurons in the hidden layer is estimated from over-constrained system of equations.The performance of the proposed technique has been evaluated using sinusoidal data and recorded speech so as to examine the spectral resolution and line splitting as well as its ability to detect voiced and unvoiced data section from a recorded speech. Results obtained show that the method can accurately resolve closely related frequencies without experiencing spectral line splitting as well as identify the voice and unvoiced segments in a recorded speech.

Performance evaluation and segmentation for synthetic aperture radar image using mixture multiscale autoregressive model and bootstrap technique

An unsupervised segmentation and its performance evaluation technique are proposed for synthetic aperture radar (SAR) image based on the mixture multiscale autoregressive (MMAR) model and the bootstrap method. The segmentation-evaluation techniques consist of detecting the number of image regains, estimating MMAR parameters by using bootstrap stochastic annealing expectation-maximization (BSAEM) algorithm, and classifying pixels into region by using Bayesian classifier. Experimental results demonstrate that the evaluation operation is robust, and the proposed segmentation method is superior to the traditional single resolution techniques, and considerably reduces the computing time over the EM algorithm.

On detecting the optimal structure of a neural network under strong statistical features in errors

The purpose of this article is to investigate the empirical performance of various statistical techniques for detecting the optimal structure of a neural network (NN) regression model. We are particularly concerned with the specification of the NN architecture when the error component is characterized by special statistical properties, such as heteroskedasticity and non-normality. We consider the sequential testing procedure based on standard Lagrange multiplier (LM) tests for neglected nonlinearity and also examine three modifications of this test that are robust to heteroskedasticity. By means of Monte Carlo simulations, we investigate the ability of these procedures to detect the right structure of the NN under different types of heteroskedasticity and noise distributions. Simulation results show that robustified LM tests allow the researcher to control the complexity of the NN without having to explicitly model all statistical aspects of the data-generating process, something which is not generally feasible with the standard LM test. The combination of robust regression-based testing with bootstrapping and generalized autoregressive conditional heteroskedasticity modelling techniques increases the efficiency of the statistical sequential procedure in eliciting the optimal NN architecture.

Ionospheric foF2 forecast over Europe based on an autoregressive modeling technique driven by solar wind parameters

The development of a new ionospheric forecasting algorithm, called the Solar Wind driven autoregression model for Ionospheric short‐term Forecast (SWIF) is presented. SWIF combines historical and real‐time ionospheric observations with solar wind parameters obtained in real time at the L1 point. This is achieved through the cooperation of an autoregression forecasting algorithm, called Time Series AutoRegressive (TSAR), with the empirical Storm Time Ionospheric Model that formulates the ionospheric storm time response based on solar wind input. The evaluation of SWIF's predictions was principally focused on its performance during selected storm time intervals over Europe. The results demonstrate significant improvement of SWIF's prediction capability over its predecessor, TSAR, significant improvement over climatology and evidence of SWIF's efficiency compared to other forecasting methods. Moreover, the evaluation of SWIF's output over long time periods, that include a wide range of geophysical conditions, suggests that SWIF can be used for prediction up to 24 h ahead.

A fault detection tool using analysis from an autoregressive model pole trajectory

A new scheme is proposed that combines autoregressive (AR) modelling techniques and pole-related spectral decomposition for the study of incipient single-point bearing defects for a vibration-based condition monitoring system. Vibration signals obtained from the ball bearings from the high vacuum (HV) and low vacuum (LV) ends of a dry vacuum pump run in normal and faulty conditions are modelled as time-variant AR series. The appearance of spurious peaks in the frequency domain of the vibration signatures translates to the onset of defects in the rolling elements. As the extent of the defects worsens, the amplitudes of the characteristic defect frequencies’ spectral peaks increase. This can be seen as the AR poles moving closer to the unit circle as the severity of the defects increase. The number of poles equals the AR model order. Although not all of the poles are of interest to the user. It is only the poles that have angular frequencies close to the characteristic bearing defect frequencies that are termed the ‘critical poles’ and are tracked for quantification of the main spectral peaks. The time-varying distance, power and frequency components can be monitored by tracking the movement of critical poles. To test the efficacy of the scheme, the proposed method was applied to increasing frame sizes of vibration data captured from a pump in the laboratory. It was found that a sample size of 4000 samples per frame was sufficient for almost perfect detection and classification when the AR poles’ distance from the centre of unit circle was used as the fault indicator. The power of the migratory poles was an alternative perfect classifier, which can be used as a fault indicator. The analysis has been validated with actual data obtained from the pump. The proposed method has interesting potential applications in condition monitoring, diagnostic and prognostic-related systems.

Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques

Electroencephalogram (EEG) analysis remains problematic due to limited understanding of the signal origin, which leads to the difficulty of designing evaluation methods. In spite of these shortcomings, the EEG is a valuable tool in the evaluation of some neurological disorders as well as in the evaluation of overall cerebral activity. In most studies, which use quantitative EEG analysis, the properties of measured EEG are computed by applying power spectral density (PSD) estimation for selected representative EEG samples. The sample for which the PSD is calculated is assumed to be stationary. This work deals with a comparative study of the PSD obtained from normal, epileptic and alcoholic EEG signals. The power density spectra were calculated using fast Fourier transform (FFT) by Welch's method, auto regressive (AR) method by Yule–Walker and Burg's method. The results are tabulated for these different classes of EEG signals.

Controlling the Heart Via the Brain: A Potential New Therapy for Orthostatic Hypotension

Electrical stimulation of the midbrain is known to influence blood pressure in animals. In humans, it is used for the treatment of chronic neuropathic pain. Our aim was to assess whether orthostatic hypotension can be successfully treated with deep brain stimulation of the periventricular/periaqueductal gray areas in humans. We recruited 11 patients who had chronic neuropathic pain and who had undergone implantation of a deep brain stimulator in the periventricular/periaqueductal gray areas. Patients were divided into three groups depending on whether they had orthostatic hypotension (one patient), mild orthostatic intolerance (five patients), or no orthostatic intolerance (five patients). Postoperatively, we continuously recorded blood pressure and heart rate with stimulation off and on and in both sitting and standing positions. From these values, we derived the blood pressure changing rate. Using autoregressive modeling techniques, we calculated changes in low- and high-frequency power spectra of heart rate and baroreflex sensitivity. Electrical stimulation reduced the decrease in systolic blood pressure on standing from 28.2 to 11.1% in one patient with orthostatic hypotension (P < 0.001). In the mild orthostatic intolerance group, an initial drop in systolic blood pressure of 15.4% was completely reversed (P < 0.001). There were no side effects in the remaining group. These changes were accompanied by increases in the blood pressure changing rate, the baroreflex sensitivity, and the baseline (sitting) low-frequency power of the RR interval, but not the high-frequency power. Electrical stimulation of the human periventricular/periaqueductal gray areas can reverse orthostatic hypotension. The cause seems to be an increase in sympathetic outflow and in baroreflex sensitivity. This has important implications for future therapies.

Prediction of human crowd pressures

Published data on accidents in human crowds that occurred over the past decade show that one of the fatal consequences of crowding was asphyxia, that is by crushing. Here a study was made of our ability to predict human crowd pressures at barriers and thereby be able to take preventive action before crushing causes injury. Utilizing the standard forward–backward autoregressive modeling techniques for spectral analysis of a measured signal, predictions of pressures generated by very high densities of pedestrians have been formulated. The study suggests that several minutes are available for corrective action to be taken to avoid an accident involving crushing of pedestrians.

Gaze-contingent control for minimally invasive robotic surgery

Objective: Recovering tissue depth and deformation during robotically assisted minimally invasive procedures is an important step towards motion compensation, stabilization and co-registration with preoperative data. This work demonstrates that eye gaze derived from binocular eye tracking can be effectively used to recover 3D motion and deformation of the soft tissue.Methods: A binocular eye-tracking device was integrated into the stereoscopic surgical console. After calibration, the 3D fixation point of the participating subjects could be accurately resolved in real time. A CT-scanned phantom heart model was used to demonstrate the accuracy of gaze-contingent depth extraction and motion stabilization of the soft tissue. The dynamic response of the oculomotor system was assessed with the proposed framework by using autoregressive modeling techniques. In vivo data were also used to perform gaze-contingent decoupling of cardiac and respiratory motion.Results: Depth reconstruction, deformation tracking, and motion stabilization of the soft tissue were possible with binocular eye tracking. The dynamic response of the oculomotor system was able to cope with frequencies likely to occur under most routine minimally invasive surgical operations.Conclusion: The proposed framework presents a novel approach towards the tight integration of a human and a surgical robot where interaction in response to sensing is required to be under the control of the operating surgeon.

CERTAINTY AND UNCERTAINTY IN MAPS: WHY WE NEED ERROR MAPS

ISEE-185 Introduction: Maps are widely used to illustrate variation in disease, in disease risk factors, and in health delivery indicators. Such maps may be descriptive of disease distribution, showing geographical variation of disease prevalence or incidence (e.g. cancer, malaria, HIV), or descriptive of geographical variation of exposures (e.g. climate, facility based health delivery, community based interventions) or part of studies investigating the relationship between disease and risk or intervention. In many cases the quantities displayed in map form, whether based on sample surveys, health information systems, or outputs from models, are subject to considerable uncertainty. Explicit mapping of the uncertainty of mapped quantities is essential for the correct interpretation of maps. Yet this information is rarely provided. Methods: We use data from three unrelated studies (primary health facility indicators of management of sexually transmitted infections in South Africa, district level variation of malaria incidence in Malawi, and malaria prevalence of infection in relation to an intensive intervention in an equatorial region) to illustrate how maps can lead to interpretation and mis-interpretation of results. We apply Bayesian autoregressive modelling techniques to produce smoothed maps and to derive uncertainty maps. We briefly outline the methodology employed. Results: Unsmoothed, smoothed and error maps from the three studies will be presented and discussed. Conclusion: Maps that are processed using only GIS approaches can be highly misleading. Uncertainty in mapped quantities should be made explicit by accompanying error maps. GIS tools generally ignore this challenge.

Southern Hemisphere Annular Mode Variability and the Role of Optimal Nonmodal Growth

Abstract The southern annular mode is the leading mode of Southern Hemisphere circulation variability, the temporal evolution of which is characterized by large amplitudes and significant persistence. Previous investigators have suggested a positive feedback mechanism that explains some of this low-frequency variance. Here, a mechanism is proposed, involving transient nonmodal growths of the anomalies, that is at least as effective as the positive feedback mechanism in increasing the low-frequency variance of the southern annular mode. Using the vector autoregressive modeling technique, a number of linear inverse models of southern annular mode variability from National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) Reanalysis 2 is derived. These models are then analyzed applying the ideas of the generalized stability theory. It is found that, as a consequence of the nonnormality of the system matrices, a significant increase in the low-frequency variance of the southern annular mode occurs through optimal nonmodal growth of the zonal wind anomalies. The nonnormality arises mainly from the relative dominance of the eddy forcing, while the nonmodal growth is caused by the interference of the nonorthogonal eigenvectors of the nonnormal system matrix. These results are demonstrated first in a simple model that retains only the two leading modes of the zonally averaged zonal wind and eddy-forcing variability, and then in a more general model that includes all the important modes. Using the more general model the authors have determined, among other things, the optimal initial perturbation and the time scale over which it experiences the maximum nonmodal growth to evolve into the pattern associated with the southern annular mode.

Autonomic function in elderly uremics studied by spectral analysis of heart rate

Aging determines an altered response of the autonomic nervous system (ANS) to physiologic stresses. A widespread autonomic damage is well recognized in chronic renal failure (CRF). We studied 30 CRF patients, aged 19 to 85 years, who were on bicarbonate hemodialysis. Surface electrocardiogram was recorded on lying and 65 degrees head-up tilt standing positions. A dedicated software, using an autoregressive modeling technique, allowed to calculate power spectral analysis (PSA) of heart rate variability, assessing a low-frequency band in the range 0.03 to 0.15 Hz, and a high-frequency band in the range 0.15 to 0.33 Hz. Low-frequency and high-frequency components are regarded, but not invariably, as specific markers of sympathetic and parasympathetic activities, respectively, and the low-frequency/high frequency ratio as an index of sympathovagal balance. In normal controls, low-frequency band value and low-frequency/high-frequency ratio on standing resulted significantly reduced in the group older than 65 years when compared with those younger than 65 years; an opposite finding was seen in high-frequency band value on standing. In uremic patients, low-frequency band on lying resulted significantly lower only in elderly uremics when compared with elderly controls, whereas low-frequency band on standing was significantly lower in elderly than in younger uremics. Regression analysis showed a significant inverse relationship between aging and most low-frequency band values, especially in uremics. The comparison of linear regression models confirmed that a sympathetic autonomic derangement is greatly present in older uremics, in particular after 50 years of age. Our data support assertion that combination of aging and CRF increases the chance of autonomic derangement being present.