Analysis of ECG signal for Detection of Cardiac Arrhythmias

Electrocardiogram (ECG), a noninvasive technique is used as a primary diagnostic tool for cardiovascular diseases. A cleaned ECG signal provides necessary information about the electrophysiology of the heart diseases and ischemic changes that may occur. It provides valuable information about the functional aspects of the heart and cardiovascular system. The objective of the thesis is to automatic detection of cardiac arrhythmias in ECG signal. Recently developed digital signal processing and pattern reorganization technique is used in this thesis for detection of cardiac arrhythmias. The detection of cardiac arrhythmias in the ECG signal consists of following stages: detection of QRS complex in ECG signal; feature extraction from detected QRS complexes; classification of beats using extracted feature set from QRS complexes. In turn automatic classification of heartbeats represents the automatic detection of cardiac arrhythmias in ECG signal. Hence, in this thesis, we developed the automatic algorithms for classification of heartbeats to detect cardiac arrhythmias in ECG signal. QRS complex detection is the first step towards automatic detection of cardiac arrhythmias in ECG signal. A novel algorithm for accurate detection of QRS complex in ECG signal is proposed in chapter 2 of this thesis. The detection of QRS complex from continuous ECG signal is computed using autocorrelation and Hilbert transform based technique. The first differential of the ECG signal and its Hilbert transformed is used to locate the R-peaks in the ECG waveform. The autocorrelation based method is used to find out the period of one cardiac cycle in ECG signal. The advantage of proposed method is to minimize the large peak of P-wave and T-wave, which helps to identify the R-peaks more accurately. Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) arrhythmias database has been used for performance analysis. The experimental result shows that the proposed method shows better performance as compared to the other two established techniques like Pan-Tompkins (PT) method and the technique which uses the difference operation method (DOM). For detection of cardiac arrhythmias, the extracted features in the ECG signal will be input to the classifier. The extracted features contain both morphological and temporal features of each heartbeat in the ECG signal. Twenty six dimension feature vector is extracted for each heartbeat in the ECG signal which consist of four temporal features, three heartbeat interval features, ten QRS morphology features and nine T-wave morphology features. Automatic classification of cardiac arrhythmias is necessary for clinical diagnosis of heart disease. Many researchers recommended Association for the Advancement of Medical Instrumentation (AAMI) standard for automatic classification of heartbeats into following five beats: normal beat (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion beat (F) and unknown beat (Q). The beat classifier system is adopted in this thesis by first training a local-classifier using the annotated beats and combines this with the global-classifier to produce an adopted classification system. The Multilayer perceptron back propagation (MLP-BP) neural network and radial basis function (RBF) neural network are used to classify the cardiac arrhythmias. Several experiments are performed on the test dataset and it is observed that MLP-BP neural network classifies ECG beats better as compared to RBF neural network

Non-invasive dynamic condition assessment techniques for railway pantographs

The railway industry desires to improve the dependability and longevity of railway pantographs by providing more effective maintenance. The problem addressed in this thesis is the development of an effective condition-based fault detection and diagnosis procedure capable of supporting improved on–condition maintenance actions. A laboratory-based pantograph test rig established during the course of the project at the University of Birmingham has been enhanced with additional sensors and used to develop and carry out dynamic tests that provide indicators that support practical pantograph fault detection and diagnosis. A 3D multibody simulation of a Pendolino pantograph has also been developed. Three distinct dynamic tests have been identified as useful for fault detection and diagnosis: (i) a hysteresis test; (ii) a frequency-response test; and (iii) a novel changing-gradient test. These tests were carried out on a new Pendolino pantograph, a used pantograph about to go for an overhaul, the new pantograph with individual parts replaced by old components, and on the new pantograph with various changes made to, for example, the greasing or chain tightness. Through a comparison of absolute measurements and features acquired from the three dynamic tests, it was possible to extract features associated with different failure modes. Finally, with a focus on the practical constraints of depot operations, a condition-based pantograph fault detection and diagnosis routine is proposed that draws on decision tree analysis. This novel testing procedure integrates the three dynamic tests and is able to identify and locate common failure modes on pantographs. The approach is considered to be appropriate for an application using an adapted version of the test rig in a depot setting