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«Detection of coronary artery disease with an electronic stethoscope Schmidt, Samuel Publication date: Document Version Publisher's PDF, also known as ...»

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Aalborg Universitet

Detection of coronary artery disease with an electronic stethoscope

Schmidt, Samuel

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Citation for published version (APA):

Schmidt, S. (2011). Detection of coronary artery disease with an electronic stethoscope. Medical Informatics

Group. Department of Health Science and Technology. Aalborg University.

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Detection of coronary artery disease with an electronic stethoscope Dissertation for the Degree of Doctor of Philosophy by Samuel Schmidt Aalborg University Faculties of Medicine Dept. of Health Science and Technology MI - Medical Informatics Group ©2011 __________________________________________

Dissertation for the Degree of Doctor of Philosophy by Samuel Schmidt Detection of coronary artery disease with an electronic stethoscope Aalborg University Faculties of Medicine Dept. of Health Science and Technology MI - Medical Informatics Group ©2011 ISBN (print edition): 978-87-7094-090-0 ISBN (electronic edition): 978-87-7094-091-7

PREFACE AND ACKNOWLEDGEMENT

The idea for this thesis originates from my final semester at the Master education in Biomedical Engineering and Informatics at Aalborg University. In our Master thesis my co-student Claus Graff and I started working in the subject of detection of coronary artery disease (CAD) with an electronic stethoscope. In a small dataset which included CAD patients and fellow students we were successful in identification of CAD patients.

This inspired me to continue research in the subject. The current Ph.D. work started in 2006 after successful completion of an additional preliminary study. The goal of my research was to develop a non-invasive method for diagnosis of CAD. In a recent

published review the research task was outlined as:

–  –  –

The result of the current thesis is not a final solution, rather a presentation of some new results and ideas which in combination with prior knowledge may contribute to a clinical useful solution.

The thesis was conducted at Department of Health Science and Technology Aalborg University from August 2006 to April 2011, with financial support from the Faculties of Engineering, Science and Medicine at Aalborg University, Coloplast A/S, Acarix A/S and the Danish National Advanced Technology Foundation.

It is a pleasure to thank you who supported and encouraged my research, even though the goal maybe appeared unrealistic. A special thanks to my supervisor Dr.

Johannes Struijk who provided admirable supervision, which guided me in the challenges of science. Thank you for the many inspiring discussions which sparked my curiosity and made me feel that I had the best job in the world. Thanks to Professor Egon Toft for the encouragements, the proficient inputs and the many times you have made use of your extensive network in the interest of the project.

I would like to thank Claus Holst-Hansen at Aalborg Hospital and Martin Græbe at Rigshospitalet Copenhagen for collecting data and for editorial feedback from a clinical view point.

In the recent years the research project expanded to include my colleges John Hansen and Henrik Zimmerman. I appreciate your enthusiastic attitude and your qualified inputs and contributions.

Thanks to the team at Coloplast A/S and Acarix A/S who was fast to realize the potential of heart sound based detection of CAD. Thank you for your support and your dedication to develop the concept beyond the scope of the current thesis.

A special thanks to my officemates Claus Graff, Mads Peter Andersen and Jonas Emborg for friendship and countless discussions about everything from personal issues to study designs and statistics.

I will thank my family and friends for their support and for reminding me of a life outside the Academic world.

Finally I express my deep appreciation to my beloved wife. I appreciate your incredible patience, your support and the warm love you show me.

Samuel Schmidt Aalborg April 8, 2011

ENGLISH SUMMERY

Coronary artery disease (CAD) is a major health problem and accounts for approximately 20% of all death in Europe. Despite of a wide range of diagnostic tests diagnostic challenges still remains. Up to 59 % of patients submitted to the invasive and costly Coronary angiography doesn’t suffer from CAD. At the same time more than 50% of people dying suddenly from CAD had no prior symptoms of CAD. The aim of the current thesis is to develop a low cost method for noninvasive diagnosis of CAD, based on analyses of heart sounds obtained with an electronic stethoscope.





Signal processing algorithms for heart sound based detection of CAD was first proposed in the early eighties. It was shown that CAD was associated with weak diastolic murmurs, which increased the high frequent sound pressure. In contrast to the prototypes used in the early studies the electronic stethoscope is robust and easy to use and thereby well suited for the clinical environment.

Since CAD is associated with diastolic murmurs identification of the diastolic periods is essential. Typical solutions for heart sound segmentation use a reference signal such as ECG, but in the case of the electronic stethoscope only the heart sound signal is available. In this thesis an automatic segmentation method, based on a duration depended Markov model, was therefore developed to divide the heart sounds into systolic and diastolic periods.

To gain further insight into the CAD murmurs an analysis was conducted to examine if cardiovascular murmurs could be modeled as a chaotic process. The result of the analysis showed no significant difference between murmurs and linear stochastic models, thereby there weren’t any indications of nonlinearity and low dimension chaos in the murmurs.

To improve robustness against noise, such as handling noise and ambient noise, a framework was developed to identify low noise periods in the heart sound signals.

Using the framework a wide range of features were extracted to discriminate heart sounds from patients with and without CAD. The analyses of the features identified a new low frequency component which discriminates between non-CAD and CAD patients. It was found that the low frequency energy (20-40 Hz) was increased in CAD subjects.

By combination of the low frequency features and a feature from a high frequency band (250-1000 Hz) a classification system was developed. In a cross validation test the area under the receiver operating characteristic curve was 0.73, the sensitivity was 72% and the specificity was 65.2%. The results indicate that the method has a potential for detection of CAD, though further improvement is necessary to solve problems related to the application of the electronic stethoscope in a clinical environment. Such challenges include better management of ambient noise and handling noise.

DANSK RESUME

Iskæmisk hjertesygdom (IHS) er et stort sundhedsproblem og tegner sig for ca. 20% af alle dødsfald i Europa. På trods af en lang række diagnostiske tests, er der stadig diagnostiske udfordringer. Op til 59% af patienterne som undergår den invasive og dyre koronarangiografi undersøgelse lider ikke af IHS. Samtidig har mere end 50% af de mennesker som dør pludselig på grund af IHS ikke tidligere haft symptomer på IHS.

Formålet med denne afhandling er at udvikle en billig ikke-invasiv metode til diagnostisering af IHS ved analyse af hjertelyde optaget med et elektronisk stetoskop.

Signalbehandlingsalgoritmer til diagnose af IHS ud fra hjertelyde blev foreslået i begyndelsen af firserne. Her blev det vist at IHS var forbundet med en svag diastolisk mislyd, hvilket øger det højfrekvente lydtryk. I modsætning til prototyperne anvendt i de tidlige undersøgelser, er et elektronisk stetoskop robust og let at bruge, hvorved det er velegnet til det kliniske miljø.

Da IHS er forbundet med diastoliske mislyde, er identifikationen af de diastoliske perioder væsentlig. Typiske løsninger til segmentering af hjertelyd bygger på et referencesignal som f.eks. EKG, men i det elektroniske stetoskops tilfælde er hjertelydsignalet det eneste signal som er til rådighed. Derfor blev en automatisk segmenteringsmetode udviklet i denne afhandling. Metoden er baseret på en tidsafhængig Markov-model, som anvendes til at opdele hjertelydene i systoliske og diastoliske perioder.

For at opnå yderligere indsigt i IHS mislydene blev en analyse udført for at undersøge om hjerte-kar-mislyde kunne modelleres som en kaotisk proces. Resultatet af analysen viste ingen signifikant forskel mellem mislyde og lineære stokastiske modeller, og dermed var der ingen tegn på ikke-linæritet og lavdimensional kaos i mislydene.

For at opnå robusthed mod støj, såsom håndteringsstøj og baggrundsstøj, blev et framework udviklet til at udtrække parameter fra støjsvage perioder i hjertelydssignaler.

Ved brug af frameworket blev en lang række parametre udvundet med det formål at diskriminere hjertelyden fra patienter med IHS fra hjertelyden fra patienter uden IHS.

Som noget nyt viste analysen af parametrene, at energiniveauet ved lave frekvenser (20-40 Hz) var forhøjet hos IHS patinterne.

Ved at kombinere de lavfrekvente parametre med parametre fra et højfrekvent frekvensbånd (250-1000 Hz), blev et klassificeringssystem udviklet. I en krydsvalideringstest var arealet under receiver operating characteristic kurven 0.73, sensitiviteten var 72%, og specificiteten var 65,2%. Resultaterne viser, at metoden har et potentiale for diagnosticering af IHS, men yderligere forbedringer er dog nødvendige for at løse problemer relateret til anvendelsen af det elektroniske stetoskop i et klinisk miljø. Disse udfordringer omfatter bl.a. en bedre håndtering af baggrundsstøj og håndteringsstøj.

TABLE OF CONTENTS

1. Introduction

1.1. Mortality and prevalence

1.2. Diagnostic challenge

1.3. Physiology and the signature of murmurs

1.4. Prior art

1.5. Scope of the current thesis

1.6. The electronic stethoscope as data collector

1.7. Preliminary study

1.8. Introduction to the studies

Introduction references

2. Study 1

3. Study 2

4. Study 3

5. Study 4

6. Study 5

7. Discussion

7.1. Segmentation

7.2. Noise and noise reduction

7.3. The potential of Nonlinear signal processing techniques

7.4. Features for detection of CAD

7.5. The cause of increased low frequency power

7.6. Clinical implication of current findings

7.7. Recommendations for new hardware

8. Conclusion

Discussion references

Appendix

Publication list

1. Introduction Coronary artery disease (CAD) is a result of extensive buildup of plaque deposits in the coronary arteries. The result is narrowed and hardened arteries which limits the coronary blood flow. CAD might result in myocardial infarction (MI) which is often caused by sudden ruptures of atrial plaque deposits.

1.1. Mortality and prevalence



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