Tahir Zaidi

Power Line Interference removal from ECG signal using SSRLS algorithm

An electrocardiogram (ECG) signal may be affected by different types of disturbances/noises e.g. Power Line Interference (PLI), baseline wander, motion artifacts etc. Removing such disturbing signals from an ECG signal is the key to its accurate analysis leading to diagnosis of potential disease(s). In this paper, we present State Space Recursive Least Squares (SSRLS) filter based method for removal of 50Hz PLI noise from an ECG signal. The results are encouraging when compared with notch filter of varying attenuation levels. Under varying PLI noise levels, the SSRLS filter is seen to show superior performance as compared to notch filter. This work is part of National Information & Communication Technologies Research and Development (ICT R&D) fund project done in collaboration with National Institute of Heart Diseases (NIHD), Pakistan.

An Intelligent Adaptive Filter for Elimination of Power Line Interference From High Resolution Electrocardiogram

Electrocardiogram (ECG) is a non-invasive method to monitor electric activities inside the heart. The signals observed on the surface of human body have very low amplitude, and thus, ECG is highly vulnerable to noise. One of the most devastating noise is power line interference (PLI) and its harmonics, which are interlaced with ECG signal even if the ECG equipment is operated on battery. The problem is further complicated when the frequency of PLI is not static, making the conventional notch filter completely ineffective. High-resolution electrocardiogram (HRECG) is a specialized technique in which higher frequency components present in the ECG signal are observed; here, we need to eliminate the harmonics of PLI as well. In this paper, we propose an intelligent adaptive noise rejection filter, which tracks and eliminates PLI as well as its harmonics. The proposed system can estimate the frequency of PLI and tune the adaptive filter for precise elimination of PLI as well as its harmonics without the requirement of an auxiliary reference input. The proposed system is based on recursive state space model, inherited with less computational complexity and performs well in a non-stationary environment. The proposed system responds well to the ongoing variations in amplitude and frequency of PLI present in the HRECG signal as well as intracardiac signal. The proposed system does not require any reference signal for tracking the PLI and its harmonics, and it is capable to self-adjust its tracking frequency for highly precise filtration of first, third, and fifth harmonics of PLI.

An intelligent adaptive filter for fast tracking and elimination of power line interference from ECG signal

The ECG (Electrocardiogram) signal reflects the electrical activity of the heart. Since amplitude of ECG signal is of order of few mV, it is susceptible to many types of noises and amongst which the most disturbing is power line interference (PLI). Variations in frequency of PLI further complicate the problem which can be taken care by implementation of adaptive notch filter (ANF). ANF normally requires a reference input which is not possible in all cases. In this paper, we have proposed an intelligent adaptive filter which does not require a reference input. Proposed method first detects the frequency of PLI noise then adaptively tracks and eliminates PLI from ECG signal.

Power Line Interference tracking in ECG signal using State Space RLS

In this paper, we present an adaptive approach to Power Line Interference (PLI) tracking and elimination from ECG signal using State Space Recursive Least Squares (SSRLS) algorithm. The key benefit is that a separate reference power line signal is not required to track PLI signal having unknown frequency. Based on sinusoidal state-spaced model, SSRLS tracks the PLI using frequency estimate provided by a separate frequency estimator. SSRLS then locks on to the phase and amplitude of PLI for its subsequent elimination from ECG signal. Computer simulations carried out on simulated ECG signal demonstrate usefulness of the idea presented.

Identification of Sudden Cardiac Arrest (SCA) using Modified Wavelet Transform

Electro Cardiogram (ECG) is used to measure and diagnose electrical activity of heart. R peak detection from ECG signal is our main goal. It is the basic mark for identification of different arrhythmias. In this paper, R wave extraction is performed by using Wavelet Transform for the identification of Sudden Cardiac Arrest (SCA). Sudden cardiac death (SCD) is a global health issue. Analysis revealed that millions of people all around the world die as the result of SCD. We need to purpose a suitable and accurate method for its identification. Modified Wavelet Transform method is used for the extraction of R peaks from ECG signal and then RR interval is extracted from ECG signal with the help of MATLAB software to identify SCA. Here a brief comparison is performed to identify SCA patient with Normal Patient. The MIT BIH database has been utilized for evaluating the algorithm.

Baseline wander removal from ECG signal using State Space Recursive Least Squares (SSRLS) adaptive filter

Removal of Baseline Wander (BLW) is the primary step in ECG signal processing. Adaptive noise cancellation (ANC) has been a popular technique for removal of noise from ECG. Conventional ANC techniques require reference signal for extraction of clean ECG. This paper investigates removal of BLW from ECG signals using State Space Recursive Least Squares (SSRLS) adaptive filter. Tracking of BLW by SSRLS has been done by using sinusoidal model in state space. Intelligent DFT has been applied for precise tuning of SSRLS filter by estimating the frequency BLW in range of 0.15 ~ 0.30 Hz. The overall scheme does not require reference signal for ECG de-noising. Successful removal of BLW from noisy ECG has been accomplished.

Differentiation between Atrial Fibrillation and Atrial Flutter using 1D Poincare Maps based on endocardial bipolar intracardiac electrograms extracted from the Right Atria

This paper presents a novel approach developed to characterize or differentiate between the two most common cardiac arrhythmias, i.e. Atrial Fibrillation (AFib) and Atrial Flutter (AFL), using endocardial bipolar intracardiac electrograms (ICEMs). The tool is based on a previously developed real time algorithm which achieves automated detection among several ICEMs originating from High Right Atria (HRA) during a standard Electrophysiology (EP) study in humans. The algorithm uses Dominant Frequency (DF) and Average Power Spectral Ratio (APSR) determined from Power Spectral Density (PSD) estimated using nonparametric spectral density estimation tools. Next, a nonlinear analysis is performed and phase portraits are developed followed by the 1D Poincare maps of the preprocessed ICEMs by sampling at their respective DFs. To further augment the automated detection process, the standard deviation for the cluster of points in 1D Poincare Map is determined. The standard deviation is found of the order of 10-2, 10-3 and 10-4 in the case of AFib, AFL, and, Atrial Tachycardia respectively. The results for endocardial bipolar preprocessed ICEMs are presented along with their statistical properties.

Nonlinear Characterization of Heart Rate Variability in Normal Sinus Rhythm, Atrial Fibrillation and Congestive Heart Failure

This paper highlights the application of methods and techniques from nonlinear analysis to illustrate their far superior capability in revealing complex cardiac dynamics under various physiological and pathological states. The purpose is to augment conventional (time and frequency based) heart rate variability analysis, and to extract significant prognostic and clinically relevant information for risk stratification and improved diagnosis. In this work, several nonlinear indices are estimated for RR intervals based time series data acquired for Healthy Sinus Rhythm (HSR) and Congestive Heart Failure (CHF), as the two groups represent different cases of Normal Sinus Rhythm (NSR). In addition to this, nonlinear algorithms are also applied to investigate the internal dynamics of Atrial Fibrillation (AFib). Application of nonlinear tools in normal and diseased cardiovascular states manifest their strong ability to support clinical decision support systems and highlights the internal complex properties of physiological time series data such as complexity, irregularity, determinism and recurrence trends in cardiovascular regulation mechanisms.Copyright

Regional Image Fusion with Genetic Algorithm Optimization

Image fusion merges the complementary information of different sensors and wavelengths. Images from multiple sensors such as visible and infrared (IR) are of particular interest in many applications. We present here a multi-stage image fusion scheme for multi-sensor images. At first stage, the proposed method segments the image into homogeneous regions and generates segmentation maps. At second stage, the segmentation maps are combined by an adaptive weight adjustment procedure. The third stage fuses the input images and segmentation maps via genetic algorithm based multi-objective optimization strategy. The results indicated that our proposed fusion scheme yields good quality fused images when compared against the existing image fusion method.

Novel algorithm for analysis and classification of atrio-ventricular nodal re-entry tachycardia (AVNRT) using intracardiac electrograms

Cardiac Electrophysiology (EP) is an established clinical technique for the examination and handling of cardiac rhythm disorders especially arrhythmias since past couple of years. Among several types of arrhythmias, Atrioventricular Nodal Reentrant Tachycardia (AVNRT) is one of the most common arrhythmia seen in the EP Lab. AVNRT is detected in EP Lab by inducing tachycardia in the patient and then by looking on monitor screen and manually evaluating its key features from recorded intra cardiac data which can indicate AVNRT presence and all this process consumes precious time of the Electrophysiologist. The proposed algorithm uses the intracardiac data and by using signal processing, it extracts the AVNRT related features which are used by the classifier for the detection of AVNRT This will save the time of manual calculations by Electrophysiologist. More than 20 patient data was used to test the algorithm for feature extraction part which shows precision between 92.8% and 96.5%. Among these 20 patients 4 belong to AVNRT and they are classified by the classifier to AVNRT successfully.