Nauman Razzaq

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.

Design & fabrication of MATLAB based solar powered CNC machine

A CNC (Computer Numerically Control) machine is a numerical control machine with the additional component of an on board PC. The PC is alluded to as the machine control unit (MCU). A novel mathematical model for the machine tool feed derive system has been devised on MATLAB. A hardware model of solar powered three axes CNC machine is designed and fabricated which uses the input from the MATLAB. CNC machine model is designed using AUTOCAD and motor driving circuit is simulated and tested using PROTEOUS. AURDUINO UNO works as a controller to move the stepper motorsin x-, y- and z- axis. Before manufacturing of CNC machine. All image processing algorithms were designed and developed using MATLAB. The control unit is tested for linear and circular interpolation by performing actual machining on CNC machine. Here is an example of the image will be perceived as a SCET test using a CNC machine.

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.

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.

Classification of Atrio-Ventricular Reentrant Tachycardia using intracardiac signals

Electrophysiology study (EPS) has been serving as a diagnostic and curative tool for rhythm-related cardiac diseases for more than few decades now. Clinical EPS requires intense knowledge of numerous protocols and run-time analysis of various parameters in order to find the root-cause of the problem. The Intracardiac Electrograms available during EPS are investigated for abnormal activations. A particular sequence of the abnormal activations hints to a specific disease. This crucial diagnosis requires a high level of expertise. In this paper, an automated algorithm to detect Atrio-Ventricular Reentrant Tachycardia (AVRT), a type of Supraventricular Tachycardia (SVT), has been proposed to assist the beginners and support the decisions of the experts of EPS. After studying the underlying medical mechanisms, exploring the electrograms formed by reentrant circuit and analyzing temporal progressions of cardiac activations of AVRT, a novel algorithm exploiting the above relationships is presented. The algorithm detects AVRT with 87.06% sensitivity.