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Heart Murmur Detection System Heart Murmur Detection/ Classification using Cochlea-like Pre-processing A THESIS SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Waqas Ahmad IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE Prof. M. Imran Hayee January 2010 © Waqas Ahmad 2010 Acknowledgements I would like to thank the Department of Electrical Engineering at the University of Minnesota Duluth (ECE at UMD) for providing me opportunity to research. I am indebted to ECE for continuously providing me support both in terms of moral and financial. I am also thankful to the University of Minnesota, School of Medicine Duluth (UMSMD) for the support in my research and experimentations. I want to extend my gratitude towards the very helpful and supportive teachers namely Dr. Stanley Burns, Dr. Glenn Nordehn, Dr. Todd Loushine, Dr. Mohammad Hassan and Dr. Jingshu Yang. Special thanks to Whiteside Institute at St. Luke‘s Hospital Duluth for generously providing the funding for this research work. My family, friends especially Nisar Ahmed, Scott Klar, office colleagues Shey Peterson, Kathy Bergh, Carol and Geni were very supportive to me I want to thank them for their encouragement throughout my stay here at UMD. Last but not the least, I want thank my advisor and mentor Dr. M Imran Hayee for his continuous guidance throughout the project and without which I would not be able to finish this research. I want to extend my thanks to the family of Dr. Imran Hayee namely his wife Hifsa Imran and kids Zarar and Shanze for inviting me to the dinner for numerous occasions. i Dedication Dedicated to family ii Abstract Accurate detection and classification of pathological heart murmurs by auscultation has been a challenge for physicians for a long time. Many research efforts have been made to apply artificial intelligence (AI) for rigorous detection/classification of heart murmurs but reported success rates have been low. All of the current AI techniques rely on converting the heart sounds to electrical signals and processing those signals via electronic circuitry of AI for murmur detection and classification. In this research, we have used a novel approach to pre-process the electrical heart sound signals by altering the electrical signal in a similar way as is done by human cochlea before they go to AI for murmur detection/classification. Cochlea-like pre-processing changes the spectral contents of the heart sound signal to enhance the murmur information which can then be detected and classified more accurately by AI circuitry. We have designed a heart murmur detection/classification system based upon this approach and have tested this system using simulated heart sounds of various murmur types. Our test results show that this approach significantly improves heart murmur detection/classification accuracy. iii Table of Contents List of Tables ...................................................................................................................... v List of Figures .................................................................................................................... vi Chapter 1 : Introduction ...................................................................................................... 1 1.1 Motivation ............................................................................................................ 1 1.2 Thesis Outline ...................................................................................................... 6 Chapter 2 : Heart Murmurs and Human Cochlea ............................................................... 8 2.1 Physiology of Heart .............................................................................................. 8 2.2 Pumping Cycle of the Heart ............................................................................... 10 2.3 Normal Heart Sound and Murmur Types ........................................................... 10 2.4 Human Cochlea .................................................................................................. 12 2.5 Existing Methods for Heart Murmur Detection/ Classification ......................... 14 2.6 Cochlea Method for Heart Murmur Detection/ Classification ........................... 15 Chapter 3 : Heart Murmur Detection System ................................................................... 18 3.1 System Overview ............................................................................................... 18 3.2 Heart Sound Signals in the Time and Frequency Domains ............................... 20 3.3 Spectrogram of Heart Sounds ............................................................................ 24 3.4 Cochlea-like Pre-Processing (CLPP) ................................................................. 26 3.5 Heart Sound Data Processing using ANN.......................................................... 28 3.6 Variable Self-Optimizing Cochlear Model for Heart Murmur Detection/ Classification...................................................................................................... 30 3.7 System Model and Hypothesis Testing .............................................................. 31 Chapter 4 : Results and Analysis ...................................................................................... 34 4.1 Variable Self-Optimizing Cochlear Model for Heart Murmur Detection/ Classification...................................................................................................... 39 Chapter 5 : Conclusion...................................................................................................... 41 5.1 Summary ............................................................................................................ 41 5.2 Future Work ....................................................................................................... 42 Bibliography ..................................................................................................................... 43 Appendix A ....................................................................................................................... 48 iv List of Tables Table 1: Driving time and distances to the closest PCI hospital......................................... 4 Table 2: Cost estimate for hospital discharges for year 2009 [1] ....................................... 5 Table 3: Heart sounds used ............................................................................................... 34 Table 4: Comparison of results from literature ................................................................. 48 v List of Figures Figure 1.1: Leading Cause of Death [1].............................................................................. 2 Figure 1.2: Deaths due to CVDs [1] ................................................................................... 2 Figure 1.3: Deaths from Heart Diseases (1900 to 2006) [1] ............................................... 3 Figure 2.1: Human heart ..................................................................................................... 8 Figure 2.2: Block diagram of pumping cycle of heart ........................................................ 9 Figure 2.3: Typical heart sound single cycle .................................................................... 10 Figure 2.4: (a) Normal Heart Sound, (b) AS and (c) AR .................................................. 11 Figure 2.7: The information perceived by human is combined output of the ear and brain ....................................................................................................................... 16 Figure 3.1: System diagram .............................................................................................. 18 Figure 3.2: (a) Normal sound, (b) Aortic Regurgitation (c) Aortic Stenosis .................... 20 Figure 3.3: Spectrum of three heart sounds ...................................................................... 22 Figure 3.4: Variance among differentiating gaps for AS and normal sound spectrum .... 23 Figure 3.5: Spectrogram of three heart sounds ................................................................. 24 Figure 3.7: CLLP frequency response with varying center frequency ............................. 27 Figure 3.8: CLPP frequency response with varying FCR................................................. 27 Figure 3.9: Artificial neural network (ANN) .................................................................... 28 Figure 3.10: Heart murmur detection using variable self-optimized cochlea model ........ 32 Figure 4.1: Three dimensional plot of accuracy, frequency capture range, and center frequency ....................................................................................................... 35 Figure 4.2: Heart murmur detection/ classification accuracy vs. frequency capture range ....................................................................................................................... 37 Figure 4.3:(a) Normal heart sound (b) AWGN (c) Normal heart sound with AWGN ..... 39 Figure 4.4: Accuracy vs. signal to noise ratio (SNR) ....................................................... 40 vi Chapter 1 : Introduction This thesis report is the study of heart murmur detection/ classification using cochlea-like pre-processing. The goal of this study is to develop a system for the detection and classification of various types of human heart murmurs among heart patients. This would be helpful to the heart patients
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