Acoustic diagnosis of aortic stenosis

Table of Contents TABLE OF CONTENTS 1.3 Origin of the Phonocardiogram & the Areas of Auscultation 3 CHAPTER 2: TIME-FREQUENCY REPRESENTATIONS 14 2.5 Comparison of the Performance of Va

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

Table of Contents

TABLE OF CONTENTS

1.3 Origin of the Phonocardiogram & the Areas of Auscultation 3

CHAPTER 2: TIME-FREQUENCY REPRESENTATIONS 14

2.5 Comparison of the Performance of Various Time-Frequency Methods

Table of Contents

3.4 The First Indicator of the Severity of the AS – The Dominant

3.5 The Second Indicator of the Severity of the AS – The Spectral Ratio

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

4.4 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

Table of Contents

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-, P-waves 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

Summary

second heart sound ( ), 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

2

/ S

SM

AVAC AVMAX / 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 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

2

/ S

SM

List of Tables

LIST OF TABLES Chapter 4

Table 1 The Spectral Ratios of the Young Volunteers with Different Cutoff

Frequencies Ranging From 25-150 Hz

45

Table 2 The Variation of the Spectral Ratios of 8 Cardiac Cycles in Normal

and Holding Breath Conditions

47

Table 3 The Hemodynamic, Acoustic Data and Particulars of the First

Subject Group

50

Table 4 Correlation of the DF of the SM with the Hemodynamic Data and

the Linear Regression Result

55

Table 5 Correlation of the SI with the Hemodynamic Data and the Linear

Regression Result

60

List of Figures

LIST OF FIGURES Chapter 1

Fig 1.5 Turbulent Velocity Signal From the Ascending Aorta of a Patient with AS 8 Fig 1.6 Schematic Diagram Showing Flow Regions Produced by a Stenosed AV 9

Chapter 2

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

Chapter 3

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

List of Figures

Chapter 4

Fig 4.1 The SR (with cutoff frequency at 60Hz) of the Systolic PCG signals of the

Young Male Volunteers with Normal Breath (green line) and Holding Breath (blue line)

46

Fig 4.3 The NCWT Plots of the SM recorded at the Aortic Area of Some Subjects

Fig 4.6 Guidelines of the 4 Indicators for the classification of the Severity of the

AS

65

List of Symbols

LIST OF SYMBOLS

b Time translation factor

)

,

(t ω

C General bilinear distribution

2

A

C 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

b

f Bandwidth parameter of Morlet wavelet

c

f Center frequency of Morlet wavelet

s

2

A

)

(ω

)

,

(b ξ

s

n Index of the discrete time-domain signal

)

,

( a b

s

NWψ NCWT distribution

)

(t

List of Symbols

V Mean velocity of the blood flow in the unobstructed portion of the artery )

(t

)

,

(t ω

)

,

( a b

s

ξ Frequency translation factor

τ Time translation factor

θ Frequency translation factor

κ Threshold factor in R-wave detection

η Threshold factor in step 2 of the multi-peak detection

µ Threshold factor in step 3 of the multi-peak detection

)

(x

ψ Conjugate of the analyzing wavelet

)

,

(θ τ

f

t

T

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