Acoustic diagnosis of aortic stenosis

ACOUSTIC DIAGNOSIS OF AORTIC STENOSIS SUN ZHANYU (B. Eng., Zhejiang University) A THESIS SUBMITTED FOR THE DEGREE OF MASTER OF ENGINEERING DEPARTMENT OF MECHANICAL ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2004 Acknowledgements ACKNOWLEDGEMENTS This study was supported with the grant from Singapore National Healthcare Group. My first appreciation must be given to my supervisor Dr. Chew Chye Heng, for his seasoned guidance. It is him who offered me this opportunity and opened a new window in my academic life. The author would like to express his sincere gratitude to Dr. Hong Geok Soon and Dr. Lim Kian Meng, who provided precious suggestions during this project. I would like to express my sincere gratitude to Dr. Poh Kian Keong and Dr. Mark Chan, who established the patient group and provided the medical records of the patients. I want to thank the FYP students – Boo Kun Ming, Teng Seok Po, Liew Shuhui, Loh Teck Kam, and Tan Chee Keong, who helped to produce the wavelet and peak plots. I would like to thank Mr Cheng, the technician for this project, for his participation in collecting the data from the hospital. The author appreciates the help rendered by the technicians in Dynamics Lab - Ahmad, Amy, and Priscilla. Finally I would like to send my gratitude to my parents for their constant encouragement and support. i Table of Contents TABLE OF CONTENTS ACKNOWLEDGEMENTS i TABLE OF CONTENTS ii SUMMARY v LIST OF TABLES vii LIST OF FIGURES viii LIST OF SYMBOLS x LIST OF ABBREVIATIONS xii CHAPTER 1: INTRODUCTION 1.1 Objective 1.2 The Cardiac Anatomy & The Cardiac Cycle 1.3 Origin of the Phonocardiogram & the Areas of Auscultation 1.4 The Dynamics of the AS 1.5 Literature Review on the Acoustic Diagnosis of AS 11 CHAPTER 2: TIME-FREQUENCY REPRESENTATIONS 14 2.1 The Fast Fourier Transform 14 2.2 The Short-Time Fourier Transform 15 2.3 Various Bilinear Distributions 18 2.4 The Continuous Wavelet Transform 20 2.5 Comparison of the Performance of Various Time-Frequency Methods on Phonocardiogram 22 ii Table of Contents CHAPTER 3: METHODOLOGY 25 3.1 Instrumentation & The Subject Groups 25 3.2 The Normalized Continuous Wavelet Transform 27 3.3 A Multi-Peak Detection Algorithm 29 3.4 The First Indicator of the Severity of the AS – The Dominant Frequency of the Systolic Murmurs 37 3.5 The Second Indicator of the Severity of the AS – The Spectral Ratio of the Systolic Murmurs 39 3.6 The Third Indicator of the Severity of the AS – The Integration of the Normalized Continuous Wavelet Transform of the Systolic Murmurs 41 3.7 The Fourth Indicator of the Severity of the AS – Combination of the Systolic Murmurs and the Second Heart Sound 42 CHAPTER 4: RESULTS & DISCUSSION 44 4.1 The Influence of the Breath Noise on the Systolic Phonocardiogram 44 4.2 The Dominant Frequency of the Systolic Murmurs 49 4.3 The Spectral Ratio of the Systolic Murmurs 52 4.4 The Integration of the Normalized Continuous Wavelet Transform of the Systolic Murmurs 60 4.5 Combination of the Systolic Murmurs and the Second Heart Sound 61 4.6 Hemodynamic Correlation 61 4.7 Clinical Application 63 4.8 Comparison with Previous Studies 67 iii Table of Contents CHAPTER 5: CONCLUSION 70 CHAPTER 6: RECOMMENDATIONS 72 BIBLIOGRAPHY 73 iv Summary SUMMARY The objective of this study is to develop a noninvasive diagnostic tool to objectively assess the severity of the aortic stenosis (AS). Specifically, the phonocardiogram (PCG) signal is measured on the chest wall and processed to extract the information to be used in the estimation of the severity of the AS. A novel multi-peak detection algorithm is developed. This multi-peak detection algorithm is of key importance in the signal processing of this study. With this multi-peak detection algorithm, the peak distribution of the normalized continuous wavelet transform (NCWT) is generated. This multi-peak detection algorithm is also used to detect the R-, T-, Pwaves of the ECG signal, which help in the localization of the systolic PCG signal. The influence of the breath noise on the systolic PCG signal is studied. It is found that the breath noise mainly contaminates the frequency content below 60Hz. Also the systolic PCG signal in the normal breath condition shows better consistency – less cycle-to-cycle variation in the spectral content than that in the holding breath condition. Therefore, the normal breath condition is more advantageous than the holding breath condition in the acoustic diagnosis of the AS. In the assessment of the severity of the AS, four indicators are developed: 1) the dominant frequency (DF) of the systolic murmurs (SM); 2) the spectral ratio (SR) of the SM; 3) the integration of the NCWT of the SM ( SI ); and 4) the combination of the SM and the v Summary second heart sound ( SM / S ), respectively. These four indicators are then correlated with the hemodynamic parameters obtained with echocardiography. The DF correlates best with the hemodynamic parameters: r = 0.63 with the maximal velocity of blood flow through aortic valve (AVMAX), r = 0.57 with the mean transaortic pressure gradient (AMPG), r = -0.72 with the aortic valve area calculated using continuity equation (AVAC), and r = 0.69 with the ratio AVMAX / AVAC . The SI correlates well with the hemodynamics parameters: r = 0.52 with AVMAX, r = 0.48 with the AMPG, r = -0.60 with AVAC, and r = 0.56 with AVMAX / AVAC . No meaningful correlations are found on the SR and the SM / S . The correlation result suggests that the DF and the SI are able to reflect the AVAC and the AVMAX, while less competent to tell the AMPG. With these four indicators, the 64 subjects under study with or without the AS of various severities are classified into three groups – severe AS, moderate AS, and other conditions. Two methods are employed in the acoustic classification. An agreement rate with the echo classification of 78% is achieved by both methods. It is suggested that although the acoustic diagnostic method developed in this study cannot accurately predict the severity of the AS, it is useful to perform the screening classification before the use of any invasive method. vi List of Tables LIST OF TABLES Chapter Table The Spectral Ratios of the Young Volunteers with Different Cutoff Frequencies Ranging From 25-150 Hz 45 Table The Variation of the Spectral Ratios of Cardiac Cycles in Normal and Holding Breath Conditions 47 Table The Hemodynamic, Acoustic Data and Particulars of the First Subject Group 50 Table Correlation of the DF of the SM with the Hemodynamic Data and the Linear Regression Result 55 Table Correlation of the SI with the Hemodynamic Data and the Linear Regression Result 60 vii List of Figures LIST OF FIGURES Chapter Fig. 1.1 Chambers and Valves of the Heart Fig. 1.2 Dynamics of a Cardiac Cycle Fig. 1.3 Best Auscultation Areas of Various Murmurs Fig. 1.4 The Anatomy of AS Fig. 1.5 Turbulent Velocity Signal From the Ascending Aorta of a Patient with AS Fig. 1.6 Schematic Diagram Showing Flow Regions Produced by a Stenosed AV Fig. 2.1 The Trade-off Between Time and Frequency Resolutions in STFT with Rectangular Window Function 17 Fig. 2.2 The WD of a Signal Composed of Two Chirps with Linearly Rising Frequency 19 Fig. 2.3 The Time and Frequency Resolutions of (a) STFT and (b) CWT 20 Fig. 2.4 The Envelope of the Coefficient Distributions of (a) Morlet Wavelet and (b) Mexican Hat with the Same Central Frequency at 50 rad/s 22 Fig. 2.5 Recognizing the Aortic Component and the Pulmonary Component of the Second Heart Sound 23 Fig. 3.1 The Recording of the Simultaneous ECG and PCG Signals of a Patient with Severe AS 26 Fig. 3.2 (a) NCWT and (b) CWT of a Chirp Signal of Constant Amplitude with Linearly Rising Frequency From 20Hz to 220Hz 28 Fig. 3.3 Multi-Peak Detection Algorithm 31 Chapter Chapter viii List of Figures Fig. 3.4 Peak Detection Results of a NCWT Plot 35 Fig. 3.5 An Example of the Characteristic ECG Waves Recognition 37 Fig. 3.6 Selecting the SM Template 39 Fig. 3.7 The Spectrum of the SM of a Patient With Severe AS 40 Chapter Fig. 4.1 The SR (with cutoff frequency at 60Hz) of the Systolic PCG signals of the 46 Young Male Volunteers with Normal Breath (green line) and Holding Breath (blue line) Fig. 4.2 The NCWT Plots of the PCG signals of BR6 48 Fig. 4.3 The NCWT Plots of the SM recorded at the Aortic Area of Some Subjects without or with AS of Different Severities 53 Fig. 4.4 The DF of the SM Vs the Hemodynamic Data 56 Fig. 4.5 The SI Vs the Hemodynamic Data 58 Fig. 4.6 Guidelines of the Indicators for the classification of the Severity of the AS 65 ix List of Symbols LIST OF SYMBOLS a Scale factor b Time translation factor c NCWT coefficient C (t , ω ) General bilinear distribution C A2 Maximum coefficient of the NCWT of the A2 d Diameter of the aortic valve orifice D Diameter of the sclerosis EH Energy of the high-frequency band EL Energy of the low-frequency band f Frequency fb Bandwidth parameter of Morlet wavelet fc Center frequency of Morlet wavelet fs Sampling Frequency f A2 Frequency of C A2 F (ω ) Spectrum G w s (b, ξ ) STFT distribution n Index of the discrete time-domain signal NWψ s (b, a ) NCWT distribution t Time s (t ) Time-domain signal x List of Symbols U Jet velocity V Mean velocity of the blood flow in the unobstructed portion of the artery w(t ) Window Function W (t , ω ) WVD distribution Wψ s (b, a ) CWT distribution ξ Frequency translation factor ω Angular frequency τ Time translation factor θ Frequency translation factor κ Threshold factor in R-wave detection η Threshold factor in step of the multi-peak detection µ Threshold factor in step of the multi-peak detection ψ ( x) Mexican Hat wavelet ψ Conjugate of the analyzing wavelet φ (θ ,τ ) Kernel function ∆f Frequency resolution ∆t Time resolution ∆T Systolic murmurs’ duration xi List of Abbreviations LIST OF ABBREVIATIONS A2 Aortic Component of the Second Heart Sound AMPG The Mean Transaortic Pressure Gradient AS Aortic Stenosis AV Aortic Valve AVAC The Aortic Valve Area Calculated Using Continuity Equation AVMAX The Maximal Velocity of The Blood Flow Through The Aortic Valve CWT Continuous Wavelet Transform DF Dominant Frequency ECG Electrocardiogram FFT Fast Fourier Transform NCWT Normalized Continuous Wavelet Transform P2 Pulmonary Component of the Second Heart Sound PCG Phonocardiogram S2 Second Heart Sound SI The Integration of the Continuous Wavelet Distribution of the SM SM Systolic Murmur SM/S2 The Combined Information of SM and S2 SR Spectral Ratio STFT Short Time Fourier Transform WD Wigner Distribution xii [...]...List of Symbols LIST OF SYMBOLS a Scale factor b Time translation factor c NCWT coefficient C (t , ω ) General bilinear distribution C A2 Maximum coefficient of the NCWT of the A2 d Diameter of the aortic valve orifice D Diameter of the sclerosis EH Energy of the high-frequency band EL Energy of the low-frequency band f Frequency fb Bandwidth parameter of Morlet wavelet fc Center frequency of Morlet... step 2 of the multi-peak detection µ Threshold factor in step 3 of the multi-peak detection ψ ( x) Mexican Hat wavelet ψ Conjugate of the analyzing wavelet φ (θ ,τ ) Kernel function ∆f Frequency resolution ∆t Time resolution ∆T Systolic murmurs’ duration xi List of Abbreviations LIST OF ABBREVIATIONS A2 Aortic Component of the Second Heart Sound AMPG The Mean Transaortic Pressure Gradient AS Aortic Stenosis. .. Center frequency of Morlet wavelet fs Sampling Frequency f A2 Frequency of C A2 F (ω ) Spectrum G w s (b, ξ ) STFT distribution n Index of the discrete time-domain signal NWψ s (b, a ) NCWT distribution t Time s (t ) Time-domain signal x List of Symbols U Jet velocity V Mean velocity of the blood flow in the unobstructed portion of the artery w(t ) Window Function W (t , ω ) WVD distribution Wψ s (b,... Transaortic Pressure Gradient AS Aortic Stenosis AV Aortic Valve AVAC The Aortic Valve Area Calculated Using Continuity Equation AVMAX The Maximal Velocity of The Blood Flow Through The Aortic Valve CWT Continuous Wavelet Transform DF Dominant Frequency ECG Electrocardiogram FFT Fast Fourier Transform NCWT Normalized Continuous Wavelet Transform P2 Pulmonary Component of the Second Heart Sound PCG Phonocardiogram... NCWT Normalized Continuous Wavelet Transform P2 Pulmonary Component of the Second Heart Sound PCG Phonocardiogram S2 Second Heart Sound SI The Integration of the Continuous Wavelet Distribution of the SM SM Systolic Murmur SM/S2 The Combined Information of SM and S2 SR Spectral Ratio STFT Short Time Fourier Transform WD Wigner Distribution xii . xi List of Abbreviations xii LIST OF ABBREVIATIONS A2 Aortic Component of the Second Heart Sound AMPG The Mean Transaortic Pressure Gradient AS Aortic Stenosis AV Aortic Valve AVAC The Aortic. support. i Table of Contents TABLE OF CONTENTS ACKNOWLEDGEMENTS i TABLE OF CONTENTS ii SUMMARY v LIST OF TABLES vii LIST OF FIGURES viii LIST OF SYMBOLS x LIST OF ABBREVIATIONS. Indicator of the Severity of the AS – The Integration of the Normalized Continuous Wavelet Transform of the Systolic Murmurs 41 3.7 The Fourth Indicator of the Severity of the AS – Combination of