Acoustic Features for the Identification of Coronary Artery Disease

Goal: Earlier studies have documented that coronary artery disease (CAD) produces weak murmurs, which might be detected through analysis of heart sounds. An electronic stethoscope with a digital signal processing unit could be a low cost and easily applied method for diagnosis of CAD. The current st...

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Published inIEEE transactions on biomedical engineering Vol. 62; no. 11; pp. 2611 - 2619
Main Authors Schmidt, Samuel E., Holst-Hansen, Claus, Hansen, John, Toft, Egon, Struijk, Johannes J.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.11.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Acoustics
Autoregressive processes
Cardiovascular disease
Complexity theory
Coronary artery disease
Coronary Artery Disease - diagnosis
Design automation
Diagnosis
Electronics
Feature extraction
Feature selection
Female
Gain
Heart
Heart Auscultation - methods
Heart sounds
Heart Sounds - physiology
Humans
Male
Medical instruments
Physical examinations
Receiving
Recording
Resonant frequency
Signal processing
Signal Processing, Computer-Assisted
Solid modeling
Stethoscopes
signal processing
Coronary artery disease
heart sounds
feature selection
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Abstract Goal: Earlier studies have documented that coronary artery disease (CAD) produces weak murmurs, which might be detected through analysis of heart sounds. An electronic stethoscope with a digital signal processing unit could be a low cost and easily applied method for diagnosis of CAD. The current study is a search for heart sound features which might identify CAD. Methods: Nine different types of features from five overlapping frequency bands were obtained and analyzed using 435 recordings from 133 subjects. Results: New features describing an increase in low-frequency power in CAD patients were identified. The features of the different types were relatively strongly correlated. Using a quadratic discriminant function, multiple features were combined into a CAD-score. The area under the receiving operating characteristic for the CAD score was 0.73 (95% CI: 0.69-0.78). Conclusion: The result confirms that there is a potential in heart sounds for the diagnosis of CAD, but that further improvements are necessary to gain clinical relevance.
AbstractList Goal: Earlier studies have documented that coronary artery disease (CAD) produces weak murmurs, which might be detected through analysis of heart sounds. An electronic stethoscope with a digital signal processing unit could be a low cost and easily applied method for diagnosis of CAD. The current study is a search for heart sound features which might identify CAD. Methods: Nine different types of features from five overlapping frequency bands were obtained and analyzed using 435 recordings from 133 subjects. Results: New features describing an increase in low-frequency power in CAD patients were identified. The features of the different types were relatively strongly correlated. Using a quadratic discriminant function, multiple features were combined into a CAD-score. The area under the receiving operating characteristic for the CAD score was 0.73 (95% CI: 0.69-0.78). Conclusion: The result confirms that there is a potential in heart sounds for the diagnosis of CAD, but that further improvements are necessary to gain clinical relevance.
Earlier studies have documented that coronary artery disease (CAD) produces weak murmurs, which might be detected through analysis of heart sounds. An electronic stethoscope with a digital signal processing unit could be a low cost and easily applied method for diagnosis of CAD. The current study is a search for heart sound features which might identify CAD. Nine different types of features from five overlapping frequency bands were obtained and analyzed using 435 recordings from 133 subjects. New features describing an increase in low-frequency power in CAD patients were identified. The features of the different types were relatively strongly correlated. Using a quadratic discriminant function, multiple features were combined into a CAD-score. The area under the receiving operating characteristic for the CAD score was 0.73 (95% CI: 0.69-0.78). The result confirms that there is a potential in heart sounds for the diagnosis of CAD, but that further improvements are necessary to gain clinical relevance.
GOALEarlier studies have documented that coronary artery disease (CAD) produces weak murmurs, which might be detected through analysis of heart sounds. An electronic stethoscope with a digital signal processing unit could be a low cost and easily applied method for diagnosis of CAD. The current study is a search for heart sound features which might identify CAD.METHODSNine different types of features from five overlapping frequency bands were obtained and analyzed using 435 recordings from 133 subjects.RESULTSNew features describing an increase in low-frequency power in CAD patients were identified. The features of the different types were relatively strongly correlated. Using a quadratic discriminant function, multiple features were combined into a CAD-score. The area under the receiving operating characteristic for the CAD score was 0.73 (95% CI: 0.69-0.78).CONCLUSIONThe result confirms that there is a potential in heart sounds for the diagnosis of CAD, but that further improvements are necessary to gain clinical relevance.
Author Schmidt, Samuel E.
Struijk, Johannes J.
Hansen, John
Holst-Hansen, Claus
Toft, Egon
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Keywords signal processing
Coronary artery disease
heart sounds
feature selection
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Snippet Goal: Earlier studies have documented that coronary artery disease (CAD) produces weak murmurs, which might be detected through analysis of heart sounds. An...
Earlier studies have documented that coronary artery disease (CAD) produces weak murmurs, which might be detected through analysis of heart sounds. An...
GOALEarlier studies have documented that coronary artery disease (CAD) produces weak murmurs, which might be detected through analysis of heart sounds. An...
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SubjectTerms Acoustics
Autoregressive processes
Cardiovascular disease
Complexity theory
Coronary artery disease
Coronary Artery Disease - diagnosis
Design automation
Diagnosis
Electronics
Feature extraction
Feature selection
Female
Gain
Heart
Heart Auscultation - methods
Heart sounds
Heart Sounds - physiology
Humans
Male
Medical instruments
Physical examinations
Receiving
Recording
Resonant frequency
Signal processing
Signal Processing, Computer-Assisted
Solid modeling
Stethoscopes
Title Acoustic Features for the Identification of Coronary Artery Disease
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