Proceedings of the International Conference on Mechanical Engineering 2011 (ICME2011) 18-20 December 2011, Dhaka, Bangladesh ICME11-RT-001 © ICME2011 1 RT-011 1. INTRODUCTION The heart is a hollow muscular organ that beats in rhythm to generate the force for pumping blood through the whole body. Each beats triggered by a bioelectric signals originated at sinoatrial (SA) node and spreads throughout the body. And the electrocardiogram (ECG or EKG) is an investigative tool for a wide range of heart conditions, from minor to life threatening that measure and records the electrical activity of the heart in superb detail. So ECG signal modeling and processing is the most significant topics in biomedical engineering. For modeling of ECG, different techniques have been developed in the past. A pole-zero models of the ECG was represented by [1] for feature extraction and data compression. Another research [2] reported that, the poles and zeros form clusters and the clusters can be related to the constituent waves of the ECG models. Transform-type methods like nonlinear transform using multiplication backward difference for detecting QRS proposed by different researchers. However, this types of modeling cannot provide a direct representation of the constituent waves in the ECG as cardiac specialist are needed for making diagnoses. Chip Away Decomposition (ChAD) algorithm which is an iterative method for Gaussian parameter determination was used for decomposing and representing the ECG model by [3]. Clifford et al. [4] used seven Gaussian functions for modeling of ECG by means of 3D state-space model which require numerical integration using a fourth-order Runge-Kutta method. S. Paravena et al. [5] used a large number of Gaussian (4 to 133) with no base line drift factor based on minimum bank method and zero crossing method. But fitting this model to the real ECG signal, starting and end point of any interval using zero crossing method is not efficient. In addition, growing number of Gaussian functions involve much time to run the program. [6] proposed a model using Gaussian function. However they cannot represent QRS wave individually as well as it is unable to fit with the real ECG at a significant level. They used double differentiation of the Gaussian function which is time consuming and need complex mathematical operation. The fitting techniques were incompetent because they were not capable to fit any negative values in their model which was quite common in real data. This paper propose a Gaussian wave base model which can simulate ECG wave as well as P, Q, R, S and T wave individually and is very simple as compared to earlier mentioned model. Nevertheless, ECG signals are corrupted by various kinds of noises like other electrical signal, , such as (i) power lines interference [7], (ii) high-frequency electromyography (EMG) noise, (iii) motion artifacts, (iv) impedance changes at the skin/electrode, (v) baseline drifts [8], (vi) electrosurgical SIMPLIFIED MATHEMATICAL MODEL for GENERATING ECG SIGNAL and FITTING THE MODEL USING NONLINEAR LEAST SQUARE TECHNIQUE Md. Abdul Awal 1 , Sheikh Shanawaz Mostafa 1 and Mohiuddin Ahmad 2 1 Department of Biomedical Engineering, Khulna University of Engineering & Technology, Khulna Bangladesh 2 Dept. of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh ABSTRACT ECG models are complex and their computational time is high. In this paper, we propose a Gaussian wave-based model which can simulate ECG wave as well as P, Q, R, S and T waves individually. In addition, this model is capable of simulating various kinds of practical phenomena. The coefficient of the model was calculated by nonlinear least square technique using Gauss-Newton algorithm. In order to evaluate the effectiveness of the model, different kind time domain and frequency domain techniques such as PSD and MSC were used. The goodness of fitting was calculated using MSE, NMSE, RMSE, NRMSE and PRD and compared with real and model ECG signal. The lower value of these error and higher cross-correlation coefficient of 0.9208 between model and real ECG indicates the outstanding performance of the model. The model is also successful in generating noisy ECG signal. Keywords: ECG Signal, Gaussian Wave, Nonlinear Least Square Technique, Goodness Of Fitting