Analysis of cardiac and epileptic signals using higher order spectra

ANALYSIS OF CARDIAC AND EPILEPTIC SIGNALS USING HIGHER ORDER SPECTRA by Chua Kuang Chua B.Eng (Hons), MSc (Dist) PhD Thesis Submitted In Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy at the Queensland University of Technology March 2010 Abstract The theory of nonlinear dyamic systems provides some new methods to handle complex systems Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals In recent years, researchers are applying the concepts from this theory to bio-signal analysis In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials It contains important insight into the state of health and nature of the disease afflicting the heart Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management Like many bio-signals, HRV signals are non-linear in nature Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots Several features were extracted from the HOS and subjected an Analysis of Variance i (ANOVA) test The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test An automated intelligent system for the identification of cardiac health is very useful in healthcare technology In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions The classifier achieved a sensitivity of 90% and a specificity of 89% This system is ready to run on larger data sets In EEG analysis, the search for hidden information for identification of seizures has a long history Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions Various methods have been proposed to predict the onset of seizures based on EEG recordings The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features About hours of EEG recordings from 10 patients were used in this study This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals These plots reveal distinct patterns The patterns are useful for visual interpretation by those without a deep ii understanding of spectral analysis such as medical practitioners It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers iii Contents ABSTRACT .I CONTENTS IV LIST OF FIGURES IX LIST OF TABLES XII STATEMENT OF AUTHORSHIP XIV ACKNOWLEDGEMENTS XV PUBLICATIONS XVI CHAPTER INTRODUCTION 1.1 Introduction 1.2 Motivation 1.3 Objectives 1.4 Contributions .5 CHAPTER BIOSIGNALS USED (HEART RATE AND EEG SIGNALS) 2.1 General 2.2 Electrocardiography 2.2.1 Data Acquisition 2.2.2 Steps in ECG Analysis 12 2.2.3 Preprocessing Of ECG .13 2.2.4 Noise Filtering Technique 18 2.2.5 QRS Complex Detection 20 2.2.6 QRS Detection Algorithm 20 iv 2.2.7 Band Pass Integer Filter 22 2.2.8 Low Pass Integer Filter 23 2.2.9 High Pass Integer Filter 23 2.2.10 Derivative 24 2.2.11 Squaring Function 24 2.2.12 Moving Window Integral 25 2.2.13 QRS Detection Using Adaptive Thresholds 25 2.2.14 Cardiac Abnormalities 26 2.2.15 Heart Rate Variability (HRV) 32 2.3 Electroencephalogram .33 2.3.1 EEG Recording Methods 34 2.3.2 Advantages of monopolar recording 35 2.3.3 Advantages of bipolar recording 35 2.3.4 EEG Lead Positioning 35 2.3.5 Classification of EEG Rhythms .36 2.3.6 Delta Waves .36 2.3.7 Theta Waves 36 2.3.8 Alpha Waves 37 2.3.9 Beta Waves 37 2.3.10 Uses of EEG 37 2.3.11 Epileptic EEG Signal 38 2.3.12 Different types of EEG Signal 41 CHAPTER LITERATURE REVIEW .44 3.1 Introduction .44 3.2 HOS and features derived from HOS 47 3.2.1 Higher order spectra .47 3.2.2 Frequency Domain Definition and Properties 51 3.2.3 Estimation of Higher-order spectra 54 v 3.3 ANALYSIS USING HOS FEATURES 55 3.3.1 Bispectrum, Bicoherence and quadratic phase coupling 55 3.4 Application of HOS on various signals .64 3.4.1 Electroencephalogram (EEG) analysis 64 3.4.2 ECG and HRV analysis 66 3.5 Summary 68 CHAPTER CLASSIFIERS .69 4.1 Gaussian mixture models (GMM): 69 4.2 Support Vector Machine (SVM): 71 CHAPTER CARDIAC STATE DIAGNOSIS USING HIGHER ORDER SPECTRA 75 5.1 Introduction .75 5.2 Data and Classes 78 5.3 Methods used for analysis 78 5.4 Statistical Analysis 81 5.5 Results .81 5.6 Discussion 90 5.6.1 The scope of the study .91 5.7 Conclusion .95 vi CHAPTER ANALYSIS OF EPILEPTIC EEG SIGNALS USING HIGHER ORDER SPECTRA 96 6.1 Data 96 6.2 Methods .97 6.3 Quantitative Analysis 97 6.4 Results .98 6.5 Discussion .105 6.6 Conclusion .107 CHAPTER CARDIAC HEALTH DIAGNOSIS USING HIGHER ORDER SPECTRA AND SUPPORT VECTOR MACHINE 108 7.1 Introduction .108 7.2 Data Acquisition Process .111 7.2.1 Preprocessing 112 7.3 Methods Used 113 7.4 Quantitative analysis .113 7.5 Support Vector Machine (SVM) Classifier 113 7.6 Principal Component Analysis 114 7.7 Test vector generation .115 7.8 Results .115 7.9 Discussion .126 7.10 Conclusion .130 vii CHAPTER AUTOMATIC IDENTIFICATION OF EPILEPTIC EEG SIGNALS USING HIGHER ORDER SPECTRA .131 8.1 Introduction .131 8.2 Data 134 8.3 HOS based features 134 8.4 Classifiers 136 8.4.1 Test vector generation 136 8.4.2 Performance comparison and Receiver Operating Characteristics (ROC) 136 8.5 Results .137 8.6 Discussion .149 8.7 Conclusion .155 CHAPTER CONCLUSION AND FUTURE WORK 156 9.1 Conclusion .156 9.2 Future work .157 REFERENCES 159 viii [98] Huang L., Sun Q., Cheng J and Huang Y., “Prediction of epileptic seizures using bispectrum analysis of electroencephalograms and artificial neural network”, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2003), pp.2947 – 2949, 2003 [99] Huber P., Kleiner B., Gasser T and Dumermuth G., “Statistical methods for investigating phase relations in stationary stochastic processes”, IEEE Transactions on Audio and Electroacoustics, Vol 19(1), pp 78 86, 1971 [100] Husar P and Henning G., “Bispectrum 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HOS 47 3.2.1 Higher order spectra .47 3.2.2 Frequency Domain Definition and Properties 51 3.2.3 Estimation of Higher- order spectra 54 v 3.3 ANALYSIS USING HOS FEATURES... intelligent system for analysis of cardiac states is very useful in diagnostics and disease management Like many bio -signals, HRV signals are non-linear in nature Higher order spectral analysis (HOS)