Detection of Atrial Fibrillation by Correlation Method

Transcription

1 e-issn Volume 2 Issue 6, June 2016 pp Scientific Journal Impact Factor : Detection of Atrial Fibrillation by Correlation Method Dr. Shahanaz Ayub1, Gaurav Guta2, Avinash Kumar Tripathi3 1,2,3 Electronics & Communication Engg., Bundelkhand Institute of Engineering & Technology Abstract This Electrocardiogram (ECG) is a useful graphical tool in the analysis of arrhythmias. Atrial Fibrillation is one of the types of Arrhythmias. In this paper, we propose the algorithm to detect Atrial Fibrillation by correlation method. In the analysis of Atrial Fibrillation (AF), two parameters are very important. One is rhythm analysis, and second is analysis of P wave. Analysis has done on five consecutive P-waves. In the analysis of atrial fibrillation, heart rhythm is irregular and, correlation coefficients of P-waves are comparatively low with respect to correlation of normal P-waves. More than two correlation coefficients are not exceeding 74%. The algorithm is implemented in MATLAB and is able to successfully detect atrial fibrillation. Keywords ECG, Arrhythmia, Atrial fibrillation, P wave, Correlation, Matlab I. INTRODUCTION In present time, most of the people are suffering by atrial fibrillation (AF). AF is arises due to improper work of atria. ECG is a graphical recording of heart electric signal. Important parameters of ECG are p-wave, QRST complex, RR interval and T-wave. In AF, heart rhythm is irregular and P-wave is not in shape. Irregular heart rhythm means variation in RR interval [1]. P-wave can detect from 350 ms to 50 ms before the R peak [2]. In AF, P-wave is not in upright shape, there are lot of local maxima. AF is most common Arrhythmia which frequency can increase with age [3][4]. Most of the time, it is responsible for heart failure. Other then heart failure, it may create some serious problem for patient. Thus detection of AF is very important at its earlier state for perfect treatment of this arrhythmia. The aim of this project is to develop a method for detecting AF that could be applied to clinical practice as a diagnostic test. The major objectives for the design of the detection method were to create a software program in MATLAB that would detect portions of a patient s electrocardiogram (ECG) that have characteristics of AF. This was achieved by heart rhythm analysis and analysis of Pwave. For AF detection, in which the RR-intervals are highly irregular, we based our algorithm on RR-interval irregularity and disturbed P-wave found in AF patients. Atrial Fibrillation can detect by rhythm analysis and P-wave analysis [5]. Nature of shape of P-wave will analyse by correlation with other standard P-wave. In this section, we introduce a methodology to analyse the rhythm and five consecutive P-waves of given ECG data [3]. ECG data from physiobank database have used in the analysis of atrial fibrillation. Samples are mainly taken from AF terminal challenge and MIT-BIH normal sinus rhythm database (nsrdb). Flow chart of work is shown by figure

2 Fig. 2 Flow chart for detection of Atrial Fibrillation For atrial fibrillation, RR- intervals were chosen as the data that would be analysed from the ECG, as this was the only noticeable difference on the ECG for fibrillation patients [6]. The design team also chose to examine RR-intervals for atrial fibrillation since most of the patient data they observed contained patterns of irregularities [7]. If rhythm is irregular, then we calculate correlation between standard P-wave and five consecutive Pwaves of ECG under test. Correlation is the phenomena to similarity between two signals. It may be positive relationship, negative relationship or no relationship. If one signal increases as the other signal increases then a positive relationship is there. If one signal increases as the other signal decreases then it is a negative relationship. A single number which determine how strong the relationship between two signals or how closely one variable related to other variable we use correlation coefficient [8]. II. DATABASE We used two data databases to evaluate the performance of the algorithm [3]. These two databases are MIT-BIH normal sinus rhythm database (nsrdb), and AF terminal challenge. In MIT- BIH normal sinus rhythm database and AF terminal challenge, ECGs are sampled at 128 Hz. These sampled data is called as digital data of ECGs. MIT- BIH normal sinus rhythm database contains 48 half-hour excerpts of two channel ambulatory ECG recording [5]. III. ANALYSIS Digitized data of ECGs are use in algorithm for ECG analysis. Analyses have done on nine AF samples with two normal samples. The following tables (I to II) provides the result related to various ECG beats rhythm analysis and tables (III to IV) provides the result related to correlation of five consecutive P-waves with standard P-wave using proposed algorithm. To measure RR intervals, digitized ECG signals were first processed to detect the R wave and its position [6][4]. This process is further repeated for detection of next R peak and its position. Thus RR interval can be calculated as RR interval = (position difference between two consecutive R peaks)* sampling time (1) 574

3 Heart rate can be calculated as HR= 60/ (RR interval) (2) HRavg= (HR1+HR2+HR3+HR4)/4 (3) In case of abnormal ECGs, RR intervals in consecutive R peaks vary drastically. Heart rate variation (%Hn) from calculated average heart rate (HRav) will exceed 7.5% at least one time in ECG strip. If rhythm is irregular, then algorithm will calculate correlation among five consecutive P-waves of ECG under test with a P-wave of standard normal ECG data. Table III provide the correlation result between ECG under test with normal ECG 16265, while Table IV provide the correlation result between ECG under test with normal ECG 803. AF Samples HR1 HR2 HR3 HR4 HRavg Af1 Af2 Af3 Af4 Af5 Af6 Af7 Af8 Af TABLE 1. Heart rate analysis of ECG samples HR1- Heart rate calculated by time interval between 1st and 2nd R peaks HR2- Heart rate calculated by time interval between 2 nd and 3rd R peaks HR3- Heart rate calculated by time interval between 3 rd and 4th R peaks HR4- Heart rate calculated by time interval between 4 th and 5th R peaks HRavg- Average Heart rate AF Samples Af1 Af2 Af3 Af4 Af5 Af6 Af7 Af8 Af9 HRavg % (HR1)v % (HR2)v % (HR3)v % (HR4)v TABLE 2. Variation of Heart rate in ECG samples HRav- Average heart rate calculated from five RR intervals 575

4 HRav =(HR1+HR2+HR3+HR4)/4 %(HR1)v- % variation in HR1 from HRav %(HR2)v- % variation in HR2 from HRav %(HR3)v- % variation in HR3 from HRav %(HR4)v- % variation in HR4 from HRav AF Samples Af1 Af2 Af3 Af4 Af5 Af6 Af7 Af8 Af9 %R1 %R2 %R3 %R4 %R TABLE 3: Correlation table between first P-wave of with AF samples AF Samples Af1 Af2 Af3 Af4 Af5 Af6 Af7 Af8 Af9 %R1 %R2 %R3 %R4 %R TABLE 4: Correlation table between first P-wave of 803 with AF samples %R1 is the correlation percentage between standard P-wave and first P-wave of ECG under test. %R2 is the correlation percentage between standard P-wave and second P-wave of ECG under test. %R3 is the correlation percentage between standard P-wave and third P-wave of ECG under test. %R4 is the correlation percentage between standard P-wave and fourth P-wave of ECG under test. %R5 is the correlation percentage between standard P-wave and fifth P-wave of ECG under test. IV. RESULT In the analysis of atrial fibrillation, heart rhythm is irregular and, correlation coefficients are comparatively low. More than two correlation coefficients are not exceeding 74%. If more than two 576

5 correlation coefficients exceed 74% then there is no atrial fibrillation. The algorithm is implemented in MATLAB and is able to successfully detect atrial fibrillation, with a fast run time, low sensitivity to noise, high accuracy, and a simple user interface. Trace of consecutive RR intervals is shown in figure Fig. 3 Correlation graph of af1with Fig. 4 Correlation graph of af1with 803 Fig. 5 Graph of rhythm for af1 577

6 Fig. 6 Correlation graph of af2with Fig. 7 Correlation graph of af2with 803 Fig. 8 Graph of rhythm for af2 578

7 Fig. 9 Correlation graph of af3with Fig. 10 Correlation graph of af3with 803 Fig. 11 Graph of rhythm for af3 579

8 Fig. 12 Correlation graph of af4with Fig. 13 Correlation graph of af4with 803 Fig. 14 Graph of rhythm for af4 580

9 Fig. 15 Correlation graph of af5with Fig. 16 Correlation graph of af5with 803 Fig. 17 Graph of rhythm for af5 581

10 Fig. 18 Correlation graph of af6 with Fig. 19 Correlation graph of af6 with 803 Fig. 20 Graph of rhythm for af6 582

11 Fig. 21 Correlation graph of af7 with Fig. 22 Correlation graph of af7 with 803 Fig. 23 Graph of rhythm for af7 583

12 Fig. 24 Correlation graph of af8 with Fig. 25 Correlation graph of af8 with 803 Fig. 26 Graph of rhythm for af8 584

13 Fig. 27 Correlation graph of af9 with Fig. 28 Correlation graph of af9 with 803 Fig. 29 Graph of rhythm for af9 585

14 V. CONCLUSIONS In the analysis of atrial fibrillation, heart rhythm is irregular and, correlation coefficients of P-waves are comparatively low. More than two correlation coefficients are not exceeding 74%. If more than two correlation coefficients exceed 74% then there is no atrial fibrillation. The algorithm is implemented in MATLAB and is able to successfully detect atrial fibrillation, with a fast run time, low sensitivity to noise, high accuracy, and a simple user interface. REFERENCE [1] [2] [3] [4] [5] [6] [7] [8] Alireza Ghodrati, Bill Murray, Stephen Marinello, RR Interval Analysis for Detection of Atrial Fibrillation in ECG Monitors, IEEE, pp , Aug JP Cauderc, S Fischer, A Costello, JP daubert, JA Konecki, W Zareba, Wavelet Analysis of Spatial Dispersion of Pwave Morphology in Patients Converted From Atrial Fibrillation, IEEE, pp , Sep Sandun Kodituwakku, Rodney A. Kennedy, Thushara D. Abhayapala, Time Frequency Analysis Compensating Missing data for Atrial Fibrillation ECG Assessment, IEEE, pp , May Shima Gholinezhadasnefestani, Kimia Nazarzadeh, Vivek Kodoth, and Ernest Lau, QRST Cancellation in ECG Signals During Atrial Fibrillation:Zero-padding vesus Time Allignment, IEEE, pp. 1-7, Dec Pankhuri Trivedi, Shahanaz Ayub, Detection of R Peak in Electrcardiogram, IJCA, vol 97, No.20, July F Beckers, W Anne, B Verheyden, C van der Dussen de Kestergat, E Van Herk, L Janssens, R Willems, H Heidbuchel, AE Aubert, Determination of Atrial Fibrillation Frequency using QRST- Cancellation with QRSScaling in Standard Electrocardiogram Leads, IEEE, pp , Sep Shahanaz Ayub, J.P.Saini, Abnormility Detection in Indian ECG using Correlation Techniques, IJCA, vol 58, pp.33-38, Nov R. Shouldice, C. Heneghan, P. Nolan, P.G. Nolan, PR and PP Intervals as Indicators of Autonomic nervous Innervationof the Cardiac Sinoatrial and Atrioventricular Nodes, IEEE, pp , March