Comparison and Performance Analysis of various ICA Algorithms for ECG signals Hemant Kasturiwale Assistant Professor,Electronics Engineering Department, Thakur College of Engineering & Technology, Kandivali (E), Zainab Mizwan Senior lecturer,Shree L.R.Tiwari college of Engineering, Mira Road (E), Abstract--The Electrocardiogram (ECG) is useful for clinical diagnosis and in biomedical research. The signals recorded are observed visually and hence can lead to wrong diagnonsis. ECG recordings are distorted by artifacts like blinking of eyes, movement of hands, dislocation of leads and so on causing serious problem for ECG interpretation and analysis. Independent component analysis is a new technique suitable for separating independent component from ECG complexes. This paper compares the various Independent Component Analysis (ICA) algorithms like FASTICA, JADE, and EFICA with respect to their capability to remove noise and artifacts from ECG. We compare the signal to interference ratio (SIR), performance index (PI), separation of semi orthogonality of these algorithms. Keywords: ECG, FASTICA, JADE, EFICA I. INTRODUCTION Biomedical signals from many sources including hearts, brains and endocrine systems pose a challenge to researchers who may have to separate weak signals arriving from multiple sources contaminated with artifacts and noise. The analysis of these signals is important both for research and for medical diagnosis and treatment [3]. Electrocardiogram (ECG or EKG) is a non-invasive test that records a nd displays the electrical activities produced by heart muscle during a cardiac cycle [4]. The ECG test is a standard clinical tool for diagnosing abnormal heart rhythms and to assess the general condition of a heart, such as myocardial infarctions, atrial enlargements, ventricular hypertrophies, and bundle branch blocks. ECGs appear to satisfy some of the conditions for ICA: 1) Current from the different sources is mixed linearly at the ECG electrodes; 2) Time delays in signal transmission are negligible; 3) There appear to be fewer sources than mixtures; and 4) Sources have non-Gaussian voltage distributions [1]. However, movements of the heart such as contraction of the chambers during beating violate the ICA assumption of spatial stationarity of the sources. The presence of moving waves of electrical activity across the heart also means that the activity of a single chamber may be taken for multiple sources by ICA. Independent component analysis (ICA) is a method for finding underlying factors or components from multivariate (multidimensional) statistical data [6]. What distinguishes ICA from other methods is that it looks for components that are both statistically independent and non- Gaussian. Fig 1: ECG Wave Wavelet analysis is a method which relies on the introduction of an appropriate basis and a characterization of the signal by the distribution of amplitude in the basis. The Wavelet Transform (WT) gives us a powerful tool to confront very diverse problems in applied sciences. It also helps to analyze the complex events occurring in different scales in the signal. Wavelet transforms are widely applied in many biomedical engineering fields for solving various real-life problems. II. INDEPENDENT COMPONENT ANALYSIS ICA is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals. ICA defines a generative model for the observed multivariate data, which is typically given as a large database of samples [1][5]. Independent Component Analysis (ICA) involves the task of computing the matrix projection of a set of components onto another set of so called independent component. ICA requires the fulfillment of two assumptions: 1) the measured signals are linear combinations of independent source signals, and 2) the independent source signals are nongaussian. A. ICA Model The ICA model is defined as follows, x (t) = A s(t) (1) 674 Vol. 3 Issue 4, April - 2014 International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 www.ijert.org IJERTV3IS040454 International Journal of Engineering Research & Technology (IJERT)