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Tim Schaeck, Michael Muma, Mengling Feng, Cuntai Guan, Abdelhak Zoubir
GOAL: An important research area in biomedical signal processing is that of quantifying the relationship between simultaneously observed time series and to reveal interactions between the signals. Since biomedical signals are potentially non-stationary and the measurements may contain outliers and artifacts, we introduce a robust time-varying generalized partial directed coherence (rTV-gPDC) function. METHODS: The proposed method, which is based on a robust estimator of the timevarying autoregressive (TVAR) parameters, is capable of revealing directed interactions between signals...
May 26, 2017: IEEE Transactions on Bio-medical Engineering
Ke Li, Jingjing Wu, Qiuju Zhang, Lei Su, Peng Chen
Remaining useful life (RUL) prediction of equipment has important significance for guaranteeing production efficiency, reducing maintenance cost, and improving plant safety. This paper proposes a novel method based on an new particle filter (PF) for predicting equipment RUL. Genetic algorithm (GA) is employed to improve the particle leanness problem that arises in traditional PF algorithms, and a time-varying auto regressive (TVAR) model and Akaike Information Criterion (AIC) are integrated to establish the dynamic model for PF...
March 28, 2017: Sensors
Guadalupe Dorantes, Martín Méndez, Alfonso Alba, J S Gonzaáez, Liborio Parrino, G Milioli
The aim of this paper is to assess heart rate variability (HRV) during the cyclic alternating pattern, which is a sleep phenomenon, composed by cortical events that interrupt the basal oscillation of the NREM sleep stage. These cortical events are called A-phases and classified into three subtypes: A1, A2, A3. In addition, a comparison between healthy and Nocturnal Front Lobe Epilepsy (NFLE) patients was carried out. HRV was assessed by means of a time-varying autoregressive (TVAR) model with an adaptive filtering prediction scheme and by the time-varying square root of the mean of the sum of the squares of differences (RMSSD) of the RR intervals...
2015: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Stefano Bregni
The Modified Allan Variance (MAVAR) was originally defined in 1981 for measuring frequency stability in precision oscillators. Due to its outstanding accuracy in discriminating power-law noise, it attracted significant interest among telecommunications engineers since the early 1990s, when it was approved as a standard measure in international standards, redressed as Time Variance (TVAR), for specifying the time stability of network synchronization signals and of equipment clocks. A dozen years later, the usage of MAVAR was also introduced for Internet traffic analysis to estimate self-similarity and long-range dependence...
April 2016: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Wenzhi Pan, Mingfei Li, Daxin Zhou, Lihua Guan, Leilei Cheng, Junbo Ge
OBJECTIVE: To evaluate the accuracy of echocardiography in identifying aortic valve structures and determine the prevalence of bicuspid aortic valves (BAV) in severe aortic stenosis (AS) population to provide useful information for transcatheteraortic valve replacement (TAVR). METHODS: A total of 300 AS patients undergoing surgical aortic valve replacement were included to determine the accuracy of transthoracic echocardiography in indentifying BAV from January 2009 to July 2013...
March 2015: Zhonghua Xin Xue Guan Bing za Zhi
A T Tzallas, V P Oikonomou, D I Fotiadis
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)...
August 2006: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Nabaraj Dahal, D Nanda Nandagopal, Bernadine Cocks, Ramasamy Vijayalakshmi, Naga Dasari, Paul Gaertner
OBJECTIVE: The objective of our current study was to look for the EEG correlates that can reveal the engaged state of the brain while undertaking cognitive tasks. Specifically, we aimed to identify EEG features that could detect audio distraction during simulated driving. APPROACH: Time varying autoregressive (TVAR) analysis using Kalman smoother was carried out on short time epochs of EEG data collected from participants as they undertook two simulated driving tasks...
June 2014: Journal of Neural Engineering
Jürgen Pauluhn
Toluene diisocyanate (TDI), a known human asthmagen, was investigated in skin-sensitized Brown Norway rats for its concentration×time (C×t)-response relationship on elicitation-based endpoints. The major goal of study was to determine the elicitation inhalation threshold dose in sensitized, re-challenged Brown Norway rats, including the associated variables affecting the dosimetry of inhaled TDI-vapor in rats and as to how these differences can be translated to humans. Attempts were made to duplicate at least some traits of human asthma by using skin-sensitized rats which were subjected to single or multiple inhalation-escalation challenge exposures...
May 7, 2014: Toxicology
Kevin Levitt, Theresa Aves, Paul Dorian, Arnold Pinter
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)...
March 2014: Journal of Electrocardiology
S Balqis Samdin, Chee-Ming Ting, Sh-Hussain Salleh, A K Ariff, A B Mohd Noor
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...
2013: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
S Castañeda-Villa, N Castaneda-Villa, R Gonzalez-Camarena, M Mejia-Avila, T Aljama-Corrales
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...
2013: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
D Gutiérrez, R Salazar-Varas
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...
2011: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Ignazio Tarantino, Georg R Linke, Jochen Lange, Ikbale Siercks, René Warschkow, Andreas Zerz
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...
September 2011: Surgical Endoscopy
Pieter van Mierlo, Evelien Carrette, Hans Hallez, Kristl Vonck, Dirk Van Roost, Paul Boon, Steven Staelens
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...
June 1, 2011: NeuroImage
Chee-Ming Ting, Sh-Hussain Salleh, Z M Zainuddin, Arifah Bahar
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)...
February 2011: IEEE Transactions on Bio-medical Engineering
Ferruccio Panzica, Giulia Varotto, Laura Canafoglia, Davide Rossi Sebastiano, Elisa Visani, Silvana Franceschetti
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...
2010: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Z G Zhang, Y S Hung, S C Chan
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...
March 2011: IEEE Transactions on Bio-medical Engineering
Jinseok Lee, Ki H Chon
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...
March 2011: IEEE Transactions on Bio-medical Engineering
Sonia Charleston-Villalobos, Guadalupe Dorantes-Méndez, Ramón González-Camarena, Georgina Chi-Lem, José G Carrillo, Tomás Aljama-Corrales
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...
January 2011: Medical & Biological Engineering & Computing
Fabrice Extramiana, Charif Tatar, Pierre Maison-Blanche, Isabelle Denjoy, Anne Messali, Patrick Dejode, Frank Iserin, Antoine Leenhardt
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...
September 2010: Europace: European Pacing, Arrhythmias, and Cardiac Electrophysiology
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