VOL. 3, NO. 3, JUNE 2008 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences © 2006-2008 Asian Research Publishing Network (ARPN). All rights reserved. www.arpnjournals.com CLASSIFICATION OF CARDIAC SIGNALS USING TIME DOMAIN METHODS B. Anuradha, K. Suresh Kumar and V. C. Veera Reddy Department of Electrical and Electronics Engineering, S.V.U. College of Engineering, Tirupati, India E-mail: anubhuma@yahoo.com ABSTRACT Electrocardiography (ECG) deals with the electrical activity of the heart. The condition of cardiac health is given by ECG and heart rate. A study of the non-linear dynamics of ECG signals for arrhythmia characterization is considered. The statistical analysis of the calculated features indicate that they differ significantly between normal heart rhythm and the different arrhythmia types and hence, can be rather useful in ECG arrhythmia detection. The discrimination of ECG signals using statistical parameters is of crucial importance in the cardiac disease therapy. The four statistical parameters considered for cardiac arrhythmia classification of the ECG signals are the standard deviation of the NN intervals (SDNN), the standard deviation of differences between adjacent NN intervals (SDSD), the root mean square successive difference of intervals which are extracted from heart rate signals (RMSSD) and the proportion derived by dividing NN50 by the total number of NN intervals (pNN50). The inclusion of Adaptive neuro fuzzy interface system (ANFIS) in the complex investigating algorithms yield very interesting recognition and classification capabilities across a broad spectrum of biomedical problem domains. Using the computed statistical parameter classification was done using Analytical method and an accuracy of 66% was achieved. The ANFIS method was compared with Analytical method. ANFIS classifier was used for the classification and an accuracy of 94% was achieved which shows that ANFIS classifier is the best of the two methods compared. Keywords: arrhythmia detection, ECG, heart rate, SDNN, SDSD, RMSD, NN50, pNN50, ANFIS. 1.0 INTRODUCTION The electrical impulse conduction in the myocardium is related to the electrical properties of the cardiac cells. The ECG is a time varying signal reflecting the ionic current flow which causes the cardiac fibers to contract and subsequently relax. 2.0 ECG CHARACTERISTICS The electrical signals described are measured by the ECG where each heart beat is displayed as a series of electrical waves characterized by peaks and valleys. An ECG gives two major kinds of information. First, by measuring time intervals on the ECG, the duration of the electrical wave crossing the heart can be determined and consequently we can determine whether the electrical activity is normal or slow, fast or irregular. Second, by measuring the amount of electrical activity passing through the heart muscle, a pediatric cardiologist may be able to find out if parts of the heart are too large or are overworked. The frequency range of an ECG signal is [0.05-100] Hz and its dynamic range is [1-10] mV. The ECG signal is characterized by five peaks and valleys labeled by successive letters of the alphabet P, Q, R, S and T. A good performance of an ECG analyzing system depends heavily upon the accurate and reliable detection of the QRS complex, as well as the T and P waves. The P wave represents the activation of the upper chambers of the heart, the atria while the QRS wave (or complex) and T wave represent the excitation of the ventricles or the lower chambers of the heart. The detection of the QRS complex is the most important task in automatic ECG signal analysis. Once the QRS complex has been identified, a more detailed examination of ECG signal, including the heart rate, the ST segment, etc., can be performed. Figure-1 shows ECG waveform characteristics and their corresponding positions in heart [1]. Figure-1. The ECG signal and its different components. 3.0 REVIEW OF LITERATURE Cuiwei Li et al., (1995) showed that with multi scale information in wavelets it is easy to characterize the ECG waves and the QRS complex. The difference from high P and T waves, noise, baseline drift and interference were recognized [2]. Senhadi et al., (1995) compared wavelet transforms for recognizing cardiac patterns. The choice of the wavelet family as well as the selection of the analyzing function into these families have been discussed to the Daubechies decompositions provided by the spline wavelet (6 levels) and the complex wavelet (10 levels) [3]. Amara Graps (1995) showed that though D6 algorithm is more complex and has a slightly higher computational overhead but it picks up detail that is missed by the Harr wavelet algorithm, which is simpler than the former. D6 of Debauchees is similar in shape to QRS complex and their energy spectrum is concentrated around low frequencies 7