Sharif Digital Repository

In this paper an Extended Kalman Filter (EKF) has been proposed for the filtering of noisy ECG signals. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. An automatic parameter selection method has also been suggested, to adapt the model with a vast variety of normal and abnormal ECG signals. The results show that the EKF output is able to track the original ECG signal shape even in the most noisiest epochs of the ECG signal. The proposed method may serve as an efficient filtering procedure for applications such as the noninvasive extraction of fetal cardiac signals from maternal abdominal signals. © 2005 IEEE  

In this paper the Extended Kalman Filter (EKF) has been used for the filtering of Electrocardiogram (ECG) signals. The method is based on a previously nonlinear dynamic model proposed for the generation of synthetic ECG signals. The results show that the EKF may be used as a powerful tool for the extraction of ECG signals from noisy measurements; which is the state of the art in applications such as the noninvasive extraction of fetal cardiac signals from maternal abdominal signals. © 2005 IEEE  

In this paper, a high performance procedure for estimating of hydrodynamic coefficients in Autonomous Underwater Vehicles (AUV's) is proposed. In modeling of an AUV, experimental data should be verified and validated using appropriate techniques. Due to implementation complexity in calculating methods, computation of hydrodynamic parameters is challenging. This paper presents analytical approaches for estimating an AUV's hydrodynamic coefficients. Nonlinear Kalman Filter (KF) algorithms are implemented to estimate unknown augmented states (coefficients). A comparative study is conducted which shows the superior performance of Unscented Kalman Filter (UKF) in comparison with Extended Kalman... 

T Wave Alternans (TWA) is considered as an indicator of Sudden Cardiac Death (SCD). In this paper for TWA detection, a method based on a nonlinear dynamic model is presented. For estimating the model parameters, we use an Extended Kalman Filter (EKF). We propose EKF6 and dualrate EKF6 approaches. Dualrate EKF is suitable for modeling the states which are not updated in all time instances. Quantitative and qualitative evaluations of the proposed method have been done on TWA challenge database. We compare our method with that proposed by Sieed et al. in TWA challenge 2008. We also compare our method with our previous proposed approach (EKF25-4obs). Results show that the proposed method can... 

In this paper an efficient filtering procedure based on Extended Kalman Filter (EKF) has been proposed. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. The proposed method considers the angular velocity of ECG signal, as one of the states of an EKF. We have considered two cases for observation equations, in one case we have assumed a corresponding observation to angular velocity state and in the other case, we have not assumed any observations for it. Quantitative evaluation of the proposed algorithm on the MIT-BIH Normal Sinus Rhythm Database (NSRDB) shows that an average SNR improvement of 8 dB is achieved for an... 

Most of the clinically useful information in Electrocardiogram (ECG) signal can be obtained from the intervals, amplitudes and wave shapes (morphologies). The automatic detection of ECG waves is important to cardiac disease diagnosis. In this paper, we propose an efficient method for extraction of characteristic points of ECG. The method is based on a nonlinear dynamic model, previously introduced for generation of synthetic ECG signals. For estimating the parameters of model, we use an Extendend Kalman Filter (EKF). By introducing a simple AR model for each of the dynamic parameters of Gaussian functions in model and considering separate states for ECG waves, the new EKF structure was... 

The automatic detection of Electrocardiogram (ECG) waves is important to cardiac disease diagnosis. A good performance of an automatic ECG analyzing system depends heavily upon the accurate and reliable detection of QRS complex, as well as P and T waves. In this paper, we propose an efficient method for extraction of characteristic points of ECG signal. The method is based on a nonlinear dynamic model, previously introduced for generation of synthetic ECG signals. For estimating the parameters of model, we use an Extendend Kalman Filter (EKF). By introducing a simple AR model for each of the dynamic parameters of Gaussian functions in model and considering separate states for ECG waves, the... 

In non-classical micro-beams, the strain energy of the system is determined by the non-classical continuum mechanics. In this study, we consider a closed-loop control methodology for suppressing the vibration of non-classical microscale Euler-Bernoulli beams with nonlinear electrostatic actuation. The non-dimensional form of the governing nonlinear partial differential equation of the system is introduced and converted into a set of ordinary differential equations using the Galerkin projection method. In addition, we prove the observability of the system and we design a state estimation system using the extended Kalman filter algorithm. The effectiveness and performance of the proposed... 

In non-classical micro-beams, the strain energy of the system is determined by the non-classical continuum mechanics. In this study, we consider a closed-loop control methodology for suppressing the vibration of non-classical microscale Euler-Bernoulli beams with nonlinear electrostatic actuation. The non-dimensional form of the governing nonlinear partial differential equation of the system is introduced and converted into a set of ordinary differential equations using the Galerkin projection method. In addition, we prove the observability of the system and we design a state estimation system using the extended Kalman filter algorithm. The effectiveness and performance of the proposed... 

In this paper, we describe a Gaussian wave-based state space to model the temporal dynamics of electrocardiogram (ECG) signals. It is shown that this model may be effectively used for generating synthetic ECGs as well as separate characteristic waves (CWs) such as the atrial and ventricular complexes. The model uses separate state variables for each CW, i.e. P, QRS and T, and hence is capable of generating individual synthetic CWs as well as realistic ECG signals. The model is therefore useful for generating arrhythmias. Simulations of sinus bradycardia, sinus tachycardia, ventricular flutter, atrial fibrillation and ventricular tachycardia are presented. In addition, discrete versions of... 

Cardiovascular diseases are one of the major causes of mortality in humans. One way to diagnose heart diseases and abnormalities is processing of cardiac signals such as electrocardiogram (ECG) signal. In many ECG analysis, location of peak, onset and offset of ECG waves must be extracted as a preprocessing step. These points are called ECG fiducial points (FPs) and convey clinically useful information. In this paper, we compare some FP extraction methods including three methods proposed recently by our research team. These methods are based on extended Kalman filter (EKF), hidden Markov model (HMM) and switching Kalman filter (SKF). Results are given for ECG signals of QT database. For all... 

In-flight aircraft angular motion estimation based on an all-accelerometers inertial measurement unit (IMU) is investigated in this study. The relative acceleration equation as the representative of a rigid-body kinematics is manipulated to present the state and measurement equations of the aircraft dynamics. A decoupled scalar form (DSF) of the system equations, as a free-singularity problem, is derived. Mathematical modeling and simulation of an aircraft dynamics, equipped with an all-accelerometers IMU, are employed to prepare measurement data. Taking into account the modeling of accelerometer error, the measurement data have been simulated as a real condition. Three extended Kalman... 

In this paper, the drivetrain identification problem of a horizontal axis gear-driven wind turbine has been considered. The identification problem leads to a precise model of the drivetrain of the wind turbines which plays a key role in the production and transmission of electrical energy. This process consists of two stages: First, offline identification which needs the input–output data from the drivetrain system. These data are obtained from the FAST code. FAST (Fatigue, Aerodynamics, Structures, and Turbulence) is a valid aeroelastic code in the simulation aeroelastic field of offshore and onshore wind turbines. In region 2 (wind velocity is between the cut-in and rated velocities), the... 

Nonlinear filtering techniques are used to fuse the Global Positioning System (GPS) with Inertial Navigation System (INS) to provide a robust and reliable navigation system with a performance superior to that of either INS or GPS alone. Prominent nonlinear estimators in this field are Kalman Filters (KF) and Particle Filters (PF). The main objective of this research is the comparative study of the well-established filtering methods of EKF, UKF, and PF based on EKF and UKF in an INS-GPS integrated navigation system. Different features of INS-GPS integrated navigation methods in the state estimation, bias estimation, and bias/scale factor estimation are investigated using these four filtering... 

By development of ocean exploration, autonomous vehicles are employed to perform on-water and underwater tasks. Using an extended Kalman filter, Inertial Navigation System/Doppler Velocity Log (INS/DVL) integrated systems are trying to navigate in oceans and underwater environments when Global Positioning System (GPS) signals are not accessible. The dependency of DVL signals on acoustic environments may cause any DVL malfunction due to sea creatures or strong wave-absorbing material. In this paper, an improved version of evolutionary TS-fuzzy (eTS) is proposed in order to predict DVL sensor outputs at DVL malfunction moment, by utilizing an artificial intelligent (AI) aided integrated... 

In this paper, we propose a novel method for extracting fiducial points (FPs) of the beats in electrocardiogram (ECG) signals using switching Kalman filter (SKF). In this method, according to McSharry's model, ECG waveforms (P-wave, QRS complex and T-wave) are modeled with Gaussian functions and ECG baselines are modeled with first order auto regressive models. In the proposed method, a discrete state variable called “switch” is considered that affects only the observation equations. We denote a mode as a specific observation equation and switch changes between 7 modes and corresponds to different segments of an ECG beat. At each time instant, the probability of each mode is calculated and... 

This paper presents efficient denoising and lossy compression schemes for electrocardiogram (ECG) signals based on a modified extended Kalman filter (EKF) structure. We have used a previously introduced two-dimensional EKF structure and modified its governing equations to be extended to a 17-dimensional case. The new EKF structure is used not only for denoising, but also for compression, since it provides estimation for each of the new 15 model parameters. Using these specific parameters, the signal is reconstructed with regard to the dynamical equations of the model. The performances of the proposed method are evaluated using standard denoising and compression efficiency measures. For... 

In this paper, a novel nonlinear joint dynamical model is presented, which is based on a set of coupled ordinary differential equations of motion and a Gaussian mixture model representation of pulsatile cardiovascular (CV) signals. In the proposed framework, the joint interdependences of CV signals are incorporated by assuming a unique angular frequency that controls the limit cycle of the heart rate. Moreover, the time consequence of CV signals is controlled by the same phase parameter that results in the space dimensionality reduction. These joint equations together with linear assignments to observation are further used in the Kalman filter structure for estimation and tracking. Moreover,...