Sharif Digital Repository

Objective: The advent of telehealth applications and remote patient monitoring has led to an increasing need for continuous signal quality monitoring to ensure high diagnostic accuracy of the recordings. Cardiovascular diseases often manifest electrophysiological anomalies, therefore the electrocardiogram (ECG) is one of the most used signals for diagnostic applications. Various types of noise and artifacts are not uncommon in ECG recordings and assessing the quality of the signal is essential prior to any clinical interpretation. In this study, a dynamic signal quality index (dSQI) is introduced using a new time-frequency template-based approach. Approach: A smoothed pseudo Wigner-Ville... 

Heart is a complex system and we can find its function in electrocardiogram (ECG) signal. The records show high mortality rate of heart diseases. So it is essential to detect and recognize ECG arrhythmias. The problem with ECG analysis is the vast variations among morphologies of ECG signals. Premature Ventricular Contractions (PVC) is a common type of arrhythmia which may lead to critical situations and contains risk. This study, proposes a novel approach for detecting PVC and visualizing data with respect to ECG morphologies by using manifold learning. To this end, the Laplacian Eigenmaps - One of the reduction method and it is in the nonlinear category - is used to extract important... 

In this paper, we proposed a method based on time-frequency dependent features extracted from Intrinsic Mode Functions (IMFs) and physiological feature such as the number of premature beats (PBs) to predict the onset of Paroxysmal Atrial Fibrillation (PAF) by using electrocardiogram (ECG) signal. To extract IMFs, we used Empirical Mode Decomposition (EMD). In order to predict PAF, we used variance of IMFs of signals, the area under the absolute of IMF curves and the number of PBs, since increasing of all of these parameters are a clear sign of PAF occurrence. We used clinical database which was provided for the 2001 Computer in Cardiology Challenge (CinC). The test set of this database... 

In order to access to an accurate detection of electrocardiogram signal in medical approaches especially mobile health and wearable medical devices, development of noise cancellation algorithms seems essential. In this study, the power line noise in ECG signal is filtered using several methods including DFT based, IIR, FIR, adaptive, Kalman, Wavelet and higher order statistics filters. Signal-to-noise ratio (SNR) improvements of the filters are then compared. It is found that FIR and IIR filtering show higher SNR improvement  

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... 

In this paper, we propose a novel method for extracting fiducial points (FPs) of electrocardiogram (ECG) signals. We propose the use of multi hidden Markov model (MultiHMM) as opposed to the traditional use of Classic HMM. In the MultiHMM method, each segment of an ECG beat is represented by a separate ergodic continuous density HMM. Each HMM has different state number and is trained separately. In the test step, the log-likelihood of two consecutive HMMs is compared and a path is estimated, which shows the correspondence of each part of the ECG signal to the HMM with the maximum log-likelihood. Fiducial points are estimated from the obtained path. For performance evaluation, the Physionet... 

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 this paper, a method for apnea bradycardia detection in preterm infants is presented based on coupled hidden semi Markov model (CHSMM). CHSMM is a generalization of hidden Markov models (HMM) used for modeling mutual interactions among different observations of a stochastic process through using finite number of hidden states with corresponding resting time. We introduce a new set of equations for CHSMM to be integrated in a detection algorithm. The detection algorithm was evaluated on a simulated data to detect a specific dynamic and on a clinical dataset of electrocardiogram signals collected from preterm infants for early detection of apnea bradycardia episodes. For simulated data, the... 

Signal compression is an important element encountered in data storage applications. Over the years various techniques for data reductions have been proposed. In this paper we introduce an effective method for compressing semi-periodic signals. Although the approach is applicable to any semi-periodic signal, our attention is focused on the compression of electrocardiogram (ECG) signals. An ECG signal is composed of many similar beats which makes it to behave semi-periodic This paper deals with beat variation periods and exploits the correlation between cycles (inter-beat) and correlation within each cycle (intra-beat) for compression. For efficient compression a 2-dimensional array is... 

This paper illustrates the use of a combined neural network model for classification of electrocardiogram (ECG) beats. We present a trainable neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care. We process a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising, a feature extraction and a classification. At first we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. Then... 

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...