TVAR

BACKGROUND: T wave variability (Tvar) is a proposed method to predict sudden cardiac death (SCD). The purpose of this trial was to evaluate the reproducibility of Tvar measurements over time and demonstrate a difference in Tvar between patient populations at risk for ventricular arrhythmias and healthy subjects. METHODS: Sixty subjects were enrolled in into 3 groups: healthy subjects (Population I), patients at high risk of SCD (Population II), and patients with a recent ventricular tachyarrhythmia event (Population III)...

This paper investigates the use of linear dynamic models (LDMs) to improve classification of single-trial EEG signals. Existing dynamic classification of EEG uses discrete-state hidden Markov models (HMMs) based on piecewise-stationary assumption, which is inadequate for modeling the highly non-stationary dynamics underlying EEG. The continuous hidden states of LDMs could better describe this continuously changing characteristic of EEG, and thus improve the classification performance. We consider two examples of LDM: a simple local level model (LLM) and a time-varying autoregressive (TVAR) state-space model...

Adventitious lung sounds (ALS) as crackles and wheezes are present in different lung alterations and their automated characterization and recognition have become relevant. In fact, recently their 2D spatial distribution (SD) imaging has been proposed to help diagnose of pulmonary diseases. In this work, independent component analysis (ICA) by infomax was used to find crackles sources and from them to apply a time variant autoregressive model (TVAR) to count and imaging the ALS. The proposed methodology was assessed on multichannel LS recordings by embedding simulated fine crackles with known SD in recorded normal breathing sounds...

The performance of EEG signal classification methods based on Common Spatial Patterns (CSP) depends on the operational frequency bands of the events to be discriminated. This problem has been recently addressed by using a sub-band decomposition of the EEG signals through filter banks. Even though this approach has proven effective, the performance still depends on the number of filters that are stacked and the criteria used to determine their cutoff frequencies. Therefore, we propose an alternative approach based on an eigenstructure decomposition of the signals' time-varying autoregressive (TVAR) models...

BACKGROUND: In laparoscopic anterior resection, minilaparotomy still is required. Recently, transvaginal hybrid natural orifice transluminal endoscopic surgery (NOTES) techniques for cholecystectomy have been described. Reports on operations that require removal of larger specimens, as in anterior resection, are scarce and limited primarily to small case series and case reports. The current study aimed to evaluate the feasibility and safety of transvaginal rigid-hybrid NOTES anterior resection (tvAR) for symptomatic diverticular disease...

Epilepsy is a neurological disorder characterized by seizures, i.e. abnormal synchronous activity of neurons in the brain. During a focal seizure, the abnormal synchronous activity starts in a specific brain region and rapidly propagates to neighboring regions. Intracranial ElectroEncephaloGraphy (IEEG) is the recording of brain activity at a high temporal resolution through electrodes placed within different brain regions. Intracranial electrodes are used to access structures deep within the brain and to reveal brain activity that cannot be observed with scalp EEG recordings...

This paper proposes non-Gaussian models for parametric spectral estimation with application to event-related desynchronization (ERD) estimation of nonstationary EEG. Existing approaches for time-varying spectral estimation use time-varying autoregressive (TVAR) state-space models with Gaussian state noise. The parameter estimation is solved by a conventional Kalman filtering. This study uses non-Gaussian state noise to model autoregressive (AR) parameter variation with estimation by a Monte Carlo particle filter (PF)...

We aimed this study at verifying the appropriateness of bivariate time-varying autoregressive models in detecting EEG-EMG relationships and identifying the characteristics of myoclonus-related EEG changes in patients with two forms of progressive myoclonus epilepsy (PME). Our results indicate that TVAR analysis was able to detect the presence of prominent peaks of EEG-EMG coherence between the EMG and contralateral frontocentral EEG derivation in all patients, revealing differences in time-frequency spectral profiles associated to the two different forms of PMEs, possibly correlated with the severity of myoclonus...

This paper proposes a new local polynomial modeling (LPM) method for identification of time-varying autoregressive (TVAR) models and applies it to time-frequency analysis (TFA) of event-related electroencephalogram (ER-EEG). The LPM method models the TVAR coefficients locally by polynomials and estimates the polynomial coefficients using weighted least-squares with a window having a certain bandwidth. A data-driven variable bandwidth selection method is developed to determine the optimal bandwidth that minimizes the mean squared error...

We present a particle filtering algorithm, which combines both time-invariant (TIV) and time-varying autoregressive (TVAR) models for accurate extraction of breathing frequencies (BFs) that vary either slowly or suddenly. The algorithm sustains its robustness for up to 90 breaths/min (b/m) as well. The proposed algorithm automatically detects stationary and nonstationary breathing dynamics in order to use the appropriate TIV or TVAR algorithm and then uses a particle filter to extract accurate respiratory rates from as low as 6 b/m to as high as 90 b/m...

In this study, a novel approach is proposed, the imaging of crackle sounds distribution on the thorax based on processing techniques that could contend with the detection and count of crackles; hence, the normalized fractal dimension (NFD), the univariate AR modeling combined with a supervised neural network (UAR-SNN), and the time-variant autoregressive (TVAR) model were assessed. The proposed processing schemes were tested inserting simulated crackles in normal lung sounds acquired by a multichannel system on the posterior thoracic surface...

AIMS: Long QT syndrome (LQTS) is a primary electrical disease characterized by QT prolongation and increased repolarization dispersion leading to T-wave amplitude beat-to-beat changes. We aimed to quantify beat-to-beat T-wave amplitude variability from ambulatory Holter recordings in genotyped LQTS patients. METHODS AND RESULTS: Seventy genotyped LQTS patients (mean age 23 +/- 15 years, 42 males, 50% LQT1, 39% LQT2, and 11% LQT3) and 70 normal matched control subjects underwent a 24-h digital Holter recording...

BACKGROUND: Sudden cardiac death in athletes is more common than in the general population. Routine screening procedures are performed to identify competitors at risk. A new Holter-based parameter analyzes variation of the ventricular repolarization (TVar). The aim of this study was to evaluate differences in electrocardiogram (ECG), Echo, and Holter (H) in competitive athletes compared to a healthy control group consisting of medical students (MS). METHODS: A total of 40 athletes (19 females, Olympic team, Luxembourg) and 40 MS (22 females) were examined by means of a resting ECG, treadmill exercise (TE), echocardiogram (Echo), as well as H recordings during a routine screening visit...

BACKGROUND: T-wave alternans is an important identifier of patients at risk of sudden cardiac death (SCD), but the procedure usually requires stress testing. In this study, the variability of T-wave amplitude (TVAR) was evaluated at rest, as a risk stratifier for SCD. METHODS: This study included 57 patients in sinus rhythm and with a left ventricular ejection fraction < or =40%, of whom 34 (60%) received an implantable cardioverter-defibrillator (ICD) after surviving SCD, and 23 (40%) presented with ischemic or nonischemic cardiomyopathy and no history of SCD...

Crackles sounds have been associated with several pulmonary pathologies and diverse algorithms have been proposed for extracting and counting them from the acquired lung sound. These tasks depend among other factors, of the relation between the magnitude of the crackle and the background lung sound. In this work, we explore multivariate signal processing to deal with the tasks and propose a new concept, the discontinuous adventitious sounds imaging. The image formation is founded on the results of two proposed methodologies that use an autoregressive (AR) model...

Several researches have tried to provide a means to analyze the second heart sound (S2) in an attempt to understand the functional mechanisms in its genesis and for diagnosis purposes. In this work we tested Time-Frequency Representation (TFR) for simulated S2 selecting and applying classical and modern TFRs such as the Spectrogram, the Wigner-Ville Distribution, the Time Varying Autoregressive (TVAR) model, the Scalogram, and the Hilbert-Huang Spectrum (HHS) by Empirical Mode Decomposition. Two performance measures are proposed, the first one based on local 2D correlations (rho) between the ideal and the estimated TFRs images, while the second one based on time moments of the TFR images to provide the normalized root-mean-square error (NRMSE)...

Several techniques have been explored to detect automatically fine and coarse crackles; however, the solution for automatic detection of crackles remains insufficient. The purpose of this work was to explore the capacity of the time-variant autoregressive (TVAR) model to detect and to provide an estimate number of fine and coarse crackles in lung sounds. Thus, simulated crackles inserted in normal lung sounds and real lung sounds containing adventitious sounds were processed with TVAR and by an expert that based crackle detection on time-expanded waveform-analysis...

This study proposes an approach to estimate airflow from respiratory sounds using time varying autoregressive modeling. Exploiting the well-established relationship between respiratory sounds and airflow, the sound signals from posterior chest and trachea are modeled using TVAR with a Fourier basis set. It is hypothesized that TVAR coefficients will give us an estimate of the amount of respiratory airflow. The procedure was tested on a group of healthy subjects and good correlation coefficients between the estimated and actual airflow were obtained...

This paper studies the time-dependent power spectral density (PSD) estimation of nonstationary surface electromyography (SEMG) signals and its application to fatigue analysis during isometric muscle contraction. The conventional time-dependent PSD estimation methods exhibit large variabilities in estimating the instantaneous SEMG parameters so that they often fail to identify the changing patterns of short-period SEMG signals and gauge the extent of fatigue in specific muscle groups. To address this problem, a time-varying autoregressive (TVAR) model is proposed in this paper to describe the SEMG signal, and then the recursive least-squares (RLS) and basis function expansion (BFE) methods are used to estimate the model coefficients and the time-dependent PSD...

The electroencephalogram (EEG) consists of an underlying background process with superimposed transient nonstationarities such as epileptic spikes (ESs). The detection of ESs in the EEG is of particular importance in the diagnosis of epilepsy. In this paper a new approach for detecting ESs in EEG recordings is presented. It is based on a time-varying autoregressive model (TVAR) that makes use of the nonstationarities of the EEG signal. The autoregressive (AR) parameters are estimated via Kalman filtering (KF)...