diseases is highly important. Electrocardiogram (ECG) is an effective tool for diagnosis of the

Transcription

1 Chapter 1 Introduction 1.1 Introduction Heart disease is one of the major reasons for high mortality rate. Accurate diagnosis of heart diseases is highly important. Electrocardiogram (ECG) is an effective tool for diagnosis of the heart abnormalities which is considered a representative signal of cardiac physiology. The state of cardiac heart is generally reflected in the shape of ECG waveform and heart rate. It may contain important pointers to the nature of diseases afflicting the heart. ECG signal consists of effective, simple, non-invasive, low-cost procedure for diagnosis of cardiovascular diseases [Korurek and Nizam, 2010]. ECG signal is used to examine ambulatory patients who are at rest during the recording or performing an exercise program and also patients in the intensive care units. These recording are examined by experts, who visually check the features of the signal and estimates the most important parameters of the signal [Fawzi, and Ali, 2009]. Bio-signals being non stationary signals, the reflection may occur at random in time scale. The symptoms of disease may show up at certain irregular intervals during the day. So ECG patterns and heart rate variability has to be observed over several hours [Ozbay, Ceylan and Kartik, 2006]. This results in enormous volume of data. So, manual interpretation of ECG signal is very tedious and time consuming. Naturally the possibility of the analyst missing or misreading the vital information is high. The errors in reading may lead to serious consequences. Moreover, there exist variations in morphology of ECG waveforms, not only of different patients or patient groups but also within the same groups [Acharya, Bhat, Iyengar et. al., 2003], [Korurek and Nizam, 2008]. Also the other factors like, access to senior/expert

2 2 cardiologist, online monitoring, diagnosis variations etc. are problems with manual reading and interpreting ECG waveforms. Computer based analysis and classification is very useful in diagnosis. It reduces the load of doctors considerably especially while analyzing the output of long term ECG signals. These computer aided diagnosis systems assist the cardiologists and senior experts by providing the necessary parameters for diagnosis or producing diagnosis [Granit, 2003]. The further inspection like heart rate variability or heart rate turbulence analysis can be more effective with the use of these systems [Mar, Zaunseder, Martinez, et.al., 2011]. 1.2 Problem description In clinical setting, such as intensive care units, it is essential for automated system to accurately detect and to classify electrocardiographic signals. Several algorithms have been developed in literature for detection and classification of arrhythmic beats. Most of them use either time or frequency domain representation of the ECG waveforms, on the basis of which many specific features are defined, allowing the recognition of beats belonging to different classes. The most difficult problem faced by today s automatic ECG analysis is the large variation in the morphologies of ECG. The ECG waveforms may differ for the same patient to such an extent that these are dissimilar to each other and at the same time these are similar for different types of beats [Ozbay, Ceylan and Karlik, 2006]. This is the main reason that the beat classifier, performing well on the training data, generalize poorly when presented with different patients ECG waveforms, not only of different patients or patients group but also within the same patient [Osowski and Linh, 2001], [Acir 2006]. This has encouraged many researchers to

3 3 continue their efforts to obtain a more precise diagnostic system for contribution to the clinical applications [Acir, 2006]. There exist different approaches for computer aided analysis of ECG signals described by researchers such as [Christov, Herrero, Krastev et.al., 2006], [Ozbay, Ceylan, and Karlik, 2006], [Ceylan, and Ozbay, 2007], [Mehta, and Lingayat, 2007], [Jekova, Bortolan, and Christov, 2008], [Joy, Chakraborty, and Ray, 2009], [Korurek, and Nizam, 2010], [Khazaee, and Ebrahimzadeh, 2010], [Mishra, and Raghav, 2010], [Moavenian, and Khorrami, 2010], [Zadeh, Khazaee, and Ranaee, 2010], [Yeh, Chiou, and Lin, 2012] etc. An effective computer aided diagnosis system requires a powerful pattern classifier as well as an efficient feature extractor that is capable of extracting important yet usually hidden information from the raw data. Even more important is the integration of suitable feature extractor and pattern classifier such that these can operate in coordination to make an effective and efficient computer aided diagnosis system [Yu, and Chou, 2008]. Therefore it is clear that there are some important issues in the design of an ECG classification methodology which, if suitably addressed, may lead to the development of more efficient system. One of these issues is related to pre-processing module. In pre-processing module, denoising methods cause distortion to ECG signal as a whole or parts thereof. Also there is need to find suitable filter or combination of filter when amount of noise is unknown [Sorensen, Johannesen, Grove, et.al., 2010]. Another issue is the extraction of suitable features and the technique of feature selection that extract significant features of lower dimensions. Another important issue is related to the

4 4 choice of suitable classifier. The classifier should be simple, fast and reliable, as using a lot of training data for developing an ECG classifier does not solve the ECG classification problem [Yu, Palreddy, and Tompkins, 1997]. 1.3 Purpose of thesis The steps required to achieve an automated arrhythmia classification include feature extraction and classification. Feature extraction stage requires a faithful characterization of the signal to achieve proper classification. However, ECG signals are contaminated /corrupted by noise and thus making it difficult to have a feature set characterising the signal. In this view, the noise removal becomes an important stage and referred to as pre-processing stage. There is need to develop pre-processing technique which can remove the noise from raw signal while keeping intact the morphology/characteristics of desired signal. The feature extraction, reduction and choice of classifier also have a great role in classification accuracy. So there is need to develop an automated ECG classification technique which can work well in noisy environment. The purpose of the thesis is to develop techniques for robust and efficient arrhythmia classification in the noisy environment. The objectives defined for this work are summarized as follows: i) Noise removal: To propose suitable technique to remove noise from ECG signals while preserving the shape of signal.

5 5 ii) Feature extraction, reduction and classification: To propose methodology for arrhythmia classification that uses suitable methods for feature extraction, feature reduction and classification. iii) Clustering technique: To propose simple and fast arrhythmia classification methodology for long term Holter monitoring. To accomplish the objectives, work has been done in these three areas. In pre-processing, a new combination of filters has been proposed for removal of noises. Powerline interference, baseline wander and other noises like muscle activity interference have been considered for this work. In the second task, a new methodology has been proposed for classification of abnormalities, in which features are extracted with application of wavelets, features are reduced with factor analysis method and linear discriminant analysis methods has been used for classification. In the third area, a new algorithm has been proposed for automatically finding the optimal number of clusters for arrhythmic classification of long term Holter ECG signals. 1.4 Contribution A new combination of filters has been proposed for the removal of baseline wander, powerline interference and other noises like muscle activity interference. The proposed method use combination of moving average and notch filter for removing noises. IIR notch when applied for removing powerline interference causes ringing effect at the starting and at the ST segment of ECG signals. But the same filter, when applied after applying moving average filter first, results in lesser ringing effect. It is further proposed to use wavelets in combination with previously designed filters for removal of other noises such as muscle activity interference.

6 6 This unique combination reduces the computational load as well as it preserves the shape of P wave, T wave and ST segment. Notch filter is of order two has been used in the present work and hence it reduces the computational load. IIR notch filter offers the best of what IIR filters have to offer; very high attenuation with a low order. Moreover, the moving average filter requires only two computations per point, regardless of the length of the filter kernel and only addition and subtraction are the operations needed, while most digital filters require timeconsuming multiplication. Hence the proposed combination filter can be considered for real time applications. Moreover, the proposed technique has been applied to various types of arrhythmic beats and it has been observed from the output waveforms as well as from various evaluation parameters that the proposed technique is suitable for the types of arrhythmic signals taken for analysis. The occurrence as well as the shape of P wave has been preserved. Thus filter can be used for indicating various atrial problems. Shape of T wave has also been preserved by proposed combination filter and can be helpful in indicating problem with beat origin and repolarization issue due to branch block. The proposed filter produces negligible changes in ST segment and can find its application for diagnostic of myocardial ischemia. For clinical applications, a methodology has been proposed for classification of cardiac abnormalities. Cardiac abnormalities alter the ECG pattern. Though there are a number of cardiac abnormalities possible, for this research work, four arrhythmic beats that is premature ventricular contraction (PVC), paced beats, left bundle branch block (LBBB) and right bundle branch block (RBBB) are considered along with normal beats for classification. Wavelets have been used for feature extraction, factor analysis method has been proposed to be used for

7 7 feature reduction and classification has been done linear discriminant analysis. The ability of the factor analysis method as a feature reduction method with principal component method and maximum likelihood method as factoring methods has been tested for both methods and that too with orthogonal rotations. A new feature reduction technique i.e. principal component factoring method with equimax rotation has been proposed in this methodology. For long term applications, a new procedure has been proposed for arrhythmia classification based on k-means. The algorithm proposed uses various similarity indices to find optimal number of clusters. The best cluster is found using cluster validity indices in next step. The best cluster has been found in lesser number of iterations than available in literature. 1.5 Standard ECG Databases Systems intended to detect or classify heartbeats or features in an ECG, are generally assessed by comparison with results from nominated reference source. For this purpose there are several publicly available databases containing pre-recorded ECG signals. These records contain annotations or labels pointing to specific locations within the signal. Well known database are CSE database, MIT-BIH Arrhythmia database and PTB diagnostic ECG database Common Standards for Quantitative Electrocardiography (CSE) Database CSE reference database consists of three types of data sets. The first data set (CSE DS-1) consists of 3 lead ECGs, and has been recorded simultaneously in the standard sequence. In the second data set (CSE DS-2), all the leads i.e. standard twelve leads plus three Frank leads are recorded simultaneously. A third CSE database (DS-3) has been developed for the assessment of diagnostic ECG and Vectorcardiogram (VCG) computer programs.

8 8 This library has been developed to standardize and evaluate the performance of computer measurement programs. It consists of 125 original 12 lead simultaneously recorded ECGs that is 1500 single lead ECGs covering a wide variety of cardiac abnormalities such as incomplete right bundle branch block, complete right bundle branch block, left anterior fascicular block, complete left bundle branch block, acute myocardial infraction, anterior myocardial infraction lateral or high-lateral myocardial infraction, apical myocardial infraction, myocardial infraction+intraventricular, conduction defect, left ventricular hypertrophy, pulmonary emphysema, ischemic ST-T changes, bigeminy, trigeminy, multiple PVC s, multiple APC s, supraventricular tachycardia, atrial flutter, atrial fibrillation, 1 st AV-block, 2 nd AV-block, Wolf-Parkinson-white syndrome, pacemaker etc. Every record of CSE ECG database is of 10 second duration. These ECG were classified by a group of five referee cardiologists and eleven different computer programs. Attention was focused on exact determination of the onsets and offsets of P, QRS and T waves. Median results of the referee s coincided best with the medians derived from all the programs studied in the CSE library and therefore combined program median can be used as a robust reference [Mehta, Shete, Lingayat, et.al., 2010]. The original ECGs of dataset 3 from CSE multilead measurement library were created without filtering and processing on ECG signals. ECG data of multilead dataset 3 were sampled at 500 Hz with a resolution of 10 bits and a maximal quantization of 5mV. The dataset 3 has 125 original ECG datasets with almost equal number of normal and various pathological cases [Sharma, Dandapat and Mahanta, 2010].

9 Massachusetts Institute of Technology / Beth Israel Hospital (MIT/BIH) Database This database [ contains several hundred ECG recordings, extended over 200 hours. Individual recordings contain one to three signals and range from 20 seconds to nearly 24 hours in length. The data has two signals and are about 30 minutes long and are annotated beat-by-beat. It consists of ten databases that serve different test purposes. Mitdb cudb nstdb stdb vfdb afdb cdb svdb ltdb odb MIT-BIH Arrhythmia Database Creighton University Ventricular Tachyarrhythmia Database MIT-BIH Noise Stress Test Database MIT-BIH ST Change Database MIT-BIH Malignant Ventricular Arrhythmia Database MIT-BIH Atrial Fibrillation/Flutter Database MIT-BIH ECG Compression Test Database MIT-BIH Supraventricular Arrhythmia Database MIT-BIH Long-Term ECG Database Other databases Out of it, MIT-BIH arrhythmia database is used for the present analysis. MIT-BIH Arrhythmia Database Record names

10 the `100 series' the `200 series' This database consists of 48 annotated records, obtained from 47 subjects studied by the Arrhythmia Laboratory of BIH in Boston during 1975 and About 60% of the records were obtained from inpatients. The database contains 23 records (the `100 series') chosen at random from a set of over hour Holter tapes and 25 records (the 200 series ) selected from the same set to include a variety of rare but clinically important phenomena that would not be well-represented by a small random sample. Several records in the 200 series were chosen specifically because features of the rhythm, QRS morphology, or signal quality may be expected to present significant difficulty to arrhythmia detectors. Each record is slightly over 30 minutes in length. Each signal file contains two signals sampled at 360 Hz. The ADCs are unipolar, with 11-bit resolution over a ±5 mv range. In most records, the upper signal is a modified limb lead II (MLII) (Figure 1.1), obtained by placing the electrodes on the chest. The lower signal is usually a modified lead V 1 (occasionally V 2 or V 5, and in one instance V 4 ); as for the upper signal, the electrodes are also placed on the chest. This configuration is routinely used by the BIH Arrhythmia Laboratory.

11 11 Fig.1.1: MIT-BIH arrhythmia database signal Database Files ECG recordings on this disk consist of three files a header file, a signal file, and an annotation file. Together these three files comprise a `record'. The.dat files are binary signal files and contain the actual data like the number of sample and the amplitude of the signal at that sample point. The.hea files are short text header files used to determine the location and format of the signal files by the software that reads them. The header files include the information about the leads used, the patient's age, sex, and medications. Finally, the.atr files are binary files containing annotations or labels pointing to specific locations within the signals. The primary software is the WFDB Software Package which contains all the functions needed to extract the data from the signal files and study the physiological signals. After installing the WFDB software in Matlab,.dat files can be converted to.mat files. Each database record contains a continuous recording from a single subject [

12 PTB diagnostic ECG database PTB (Physikaliscg-TechnischeBundesanstalt) diagnostic ECG database (ptb-db) [ is provided by the National Institute of Germany. It contains 549 records from 290 subjects (209 male, 81female) with the age varies from 17 to 87 years (Table 1.1). Each record has conventional 12 lead ECG measurements along with three Frank leads V x, V y and V z simultaneously. Each signal was digitised at 1000 samples per second with 16 bit resolution over a range of ± mv. The clinical history and diagnosis is also provided for most of the subjects. Number of subject Table 1.1 Diagnosis details of PTB database Diagnostic class 52 Healthy control 148 Myocardial infraction 15 Bundle branch block 18 Cardiomyopathy/ Heart failure 14 Dysrhythmia 7 Myocardial hypertrophy 6 Valvular heart disease 4 Myocarditis 4 Miscellaneous 22 Not available 1.6 Organization of thesis The thesis is divided into seven chapters. The organization of the thesis is as follows:

13 13 i) Chapter 1: This chapter gives the introduction to the thesis, problem description, purpose, overview of the contribution and layout of the thesis report. This chapter also discusses the database available for analysis and selection of database. ii) Chapter 2: It presents the introduction of the anatomy of heart, electrocardiography signal s characteristics and lead placement. It addresses heart abnormalities and various types of noises associated with the recorded ECG signal. iii) Chapter 3: The relevant research work carried out by different researchers has been presented in this chapter under the heading literature survey. iv) Chapter 4: In this chapter, a combination filtering technique for removal of baseline wander, powerline interference and other noises like muscle activity interference is proposed. The performance of the filter is calculated in terms of various performance parameters and waveforms are shown for different types of ECG beats. v) Chapter 5: It discusses classification of arrhythmia using linear discriminant analysis as classifier. The features extracted with wavelets are reduced using the factor analysis method before the classification. Various orthogonal rotations are used for feature reduction. vi) Chapter 6: It deals with classification of arrhythmias using clustering techniques. The comparison of the two techniques i.e. hierarchical and k-means is done at the beginning. A new algorithm is proposed for finding optimal number of clusters and best cluster is determined by using validity indices. vii) Chapter 7: In this chapter, the overall conclusions of the work are presented and possible future research directions are indicated.