IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 9, Issue 3, Ver. IV (May - Jun. 2014), PP 61-66 www.iosrjournals.org www.iosrjournals.org 61 | Page ECG Analysis Based On Window Filtering Approach Using Empirical Mode Decomposition Technique Rovin Tiwari 1 , Prof. Rahul Dubey 2 , Prof. N.K. Mittal 3 1,2,3 Department of Electronics and communication, OIST, Bhopal Abstract: Electrocardiogram (ECG) signal is a very important measure to know the Heart actual conditions. It becomes necessary to make ECG signals free from noise for proper analysis and detection of the diseases. In this paper a new approach based on the window filtering using Empirical Mode Decomposition technique is presented. EMD is a relatively new, data driven adaptive technique used to decompose ECG signal into a series of Intrinsic Mode Functions (IMFs). Different ECG signals are used to verify the proposed method using MATLAB software. Method presented in this paper is based on rectangular and Kaiser Window filtering method to ECG signal and calculate mean square value using Hard and soft Thresholding. For analyses purpose, extensive simulations are carried out using the MIT-BIH database and the performances are evaluated in terms of standard metrics namely, Features of signal, SNR improvement in dB, Mean Square Error (MSE) and Percent Root Mean Square Difference (PRD). Keywords : ECG, Window filtering, Rectangular, Kaiser, EMD, CSE and MIT-BIH database. I. Introduction An electrocardiogram is used to monitor your heart. Each beat of our heart is triggered by an electrical impulse normally generated from special cells in the upper right chamber of your heart. An electrocardiogram — also called an ECG or EKG shows in figure1.[11] Figure1.Different features in ECG signal Automatic classification ECG signal consist of different features of ECG in one cardiac cycle. Features relating to fiducial point intervals were considered for each heart beat Features relating to heartbeat intervals and ECG morphology were also calculated separately for each heartbeat in the ECG signals. The RR-interval is the time between successive RR-peaks, the inverse of this time interval gives the instantaneous heart rate. A series of RR-intervals is known as a RR tachogram and variability of these RR- intervals reveals important information about the physiological state of the subject. In this paper we generate a synthetic ECG signal with realistic PQRSTU morphology. SNR is the powerful parameter to decide the quality and analysis of signal. By using this proposed method the SNR is obtained highest. This paper deals with the study of FIR filtering of ECG signals. The performance of FIR filter is evaluated on several ECGs recordings, and studying the SNR and morphology of the filter outputs. We find FIR filter with rectangular and Kaiser Window works excellent [2]. EMD is intuitive and adaptive, with basic functions derived fully from the data.. The key task here is to identify the intrinsic oscillatory modes by their characteristic time scales in the signal empirically, and accordingly, decompose the signal into intrinsic mode functions (IMFs) [1]. A function is considered to be an IMF if it satisfies two conditions [9]; First, In the whole data set, the number of local extrema and that of zero crossings must be equal to each other or different by at most one and second, at any point, the mean value of the envelope defined by the local maxima and that defined by the local minima should be zero. Windowing of a