Application of the ARMA method to acoustic detection of coronary artery disease

  • M. Akay
  • W. Welkowitz
  • J. L. Semmlow
  • J. Kostis
Physiological Measurement


To further explore the application of advanced signal processing techniques to the noninvasive detection of coronary artery disease, 30 patients (10 angioplasty and 20 normal or abnormal) were tested using autoregressive moving average (ARMA) modelling of the disastolic heart sound data. It is during diastole that coronary blood flow is maximum and sounds associated with turbulent blood flow through partially occluded coronary arteries would be loudest. Model parameters (the power spectral density (PSD) functions and the poles of the ARMA method) were used to separate the normal patients from the abnormal patients in the normal/ abnormal study, or to decide whether the recordings were made before or after angioplasty in the angioplasty study. The decisions were made ‘blind’, without knowledge of the actual disease states of the patients for the normal/abnormal study and without prior knowledge of whether a given recording was made before or after angioplasty for the angioplasty study. Results from the angioplasty and the normal/abnormal studies showed that pre- and post-angioplasty records were correctly distinguished in 8 out of 10 cases, and normal and abnormal records were correctly distinguished in 17 of 20 cases. These results also confirmed that high frequency energy above 400 Hz is probably associated with coronary stenosis.


Coronary artery disease Noninvasive detection Sound 


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  1. Akay, M., Bauer, M., Semmlow, J. L., Welkowitz, W. andKostis, J. (1988a) AR modeling of diastolic heart sound.Proc. IEEE Frontiers in Medicine, New Orleans, 72–175.Google Scholar
  2. Akay, M., Bauer, M., Semmlow, J. L., Welkowitz, W. andKostis, J. (1988b) Analysis of diastolic heart sounds before and after angioplasty.Proc. IEEE Frontiers in Medicine, New Orleans, 257–260.Google Scholar
  3. Akay, M., Semmlow, J. L., Welkowitz, W. andKostis, J. (1989) Parametric analysis of diastolic heart sounds before and after angioplasty.Proc. IEEE Frontiers in Medicine, Seattle, 51–53.Google Scholar
  4. Akay, M., Semmlow, J. L., Welkowitz, W., Bauer, M. andKostis, J. (1990a) Detection of coronary occlusions using AR modelling of diastolic heart sounds.IEEE Trans. Biomed. Eng.,BME-37, 366–373.CrossRefGoogle Scholar
  5. Akay, M., Semmlow, J. L., Welkowitz, W., Bauer, M. andKostis, J. (1990b) Noninvasive detection of coronary occlusions using eigenvector methods before and after angioplasty.IEEE Trans. on Biomed. Eng.,BME-37, 1095–1104.CrossRefGoogle Scholar
  6. Akay, M. (1990) Noninvasive detection of coronary artery disease using advanced signal processing methods. Ph.D. Dissertation, Rutgers University, Piscataway, New Jersey.Google Scholar
  7. Box G. andPierce, D. (1970) Distribution of residual autocorrelations in autoregressive-integrated moving average time series models.J. Am. Statist. Assoc.,64, 122–145.MathSciNetGoogle Scholar
  8. Bruzzone, S. P. andKaveh, M. (1984) Information tradeoffs in using the sample autocorrelation function in ARMA parameter estimation.IEEE Trans. Acoustics, Speech, Signal Processing,ASSP-32, 701–715.MathSciNetCrossRefGoogle Scholar
  9. Cheng, T. O. (1970) Diagnostic murmur caused by coronary artery stenosis.Ann. Intern. Med.,72, 543–546.Google Scholar
  10. Dock, W. andZoneraich, S. (1967) A diastolic murmur arising in a stenosed coronary artery.Am. J. of Med.,42, 617.CrossRefGoogle Scholar
  11. Duncan, G. W., Gruber, T. O., Dewey, C. F., Myers, G. S. andLees, R. S. (1975) Evaluation of cartoid stenosis by phonoangiography.New England J. of Med.,27, 1124–1128.CrossRefGoogle Scholar
  12. Friedlander, B. andPorat, (1984) Modifield Yule-Walker of ARMA spectral estimator.IEEE Trans. Aerospace, Electron, Syst.,AES-20, 158–172.MathSciNetGoogle Scholar
  13. Giddens, D. P., Mabon, R. F. andCassanova, R. A. (1976) Measurements of disordered flows distal to subtotal vascular stenoses in the thoracic aortas of dogs.Circulation Research,39(1), 112–119.Google Scholar
  14. Izraelevitz, D. andLim J. S. (1983) Spectral characteristics of the overdetermined normal equation method for spectral estimation. Proc. 2nd ASSP Spectral Estimator Workshop, 49–54.Google Scholar
  15. Kartchner, M. M., McRae, L. P., Morrison, F. D. (1973) Noninvasive detection and evaluation of carotid occlusive disease.Arch. Surg.,106, 528–535.Google Scholar
  16. Kaveh, M. (1979) High resolution estimator for noisy signals.IEEE Trans. Acoustic, Speech, Signal Processing,ASSP-27, 286–297.CrossRefGoogle Scholar
  17. Kaveh, M. andBruzzone, S. P. (1981) A comparative overview of ARMA spectral estimators. Proc. 1st ASSP Spectral Estimation Workshop, 2.4.1–2.4.8.Google Scholar
  18. Kay, S. M. andMarple, S. L. (1981) Spectral analysis: a modern perspective.Proc. IEEE,69, 1380–1419.CrossRefGoogle Scholar
  19. Kendall, M. andStuart, A. (1977)The advanced theory of statistics. 4th edition, Griffin and Co., London.zbMATHGoogle Scholar
  20. Khalifa, A. M. A. andGiddens, D. P. (1981) Characterization of poststenotic flow disturbances.J. Biomechanics,14, 275–296.Google Scholar
  21. Kurtz, K. J. (1984) Dynamic vascular auscultation.Am. J. Med.,76, 1066–1074.CrossRefGoogle Scholar
  22. Lees, R. S. andDewey, Jr.C. F. (1970) Phonoangiography: A new noninvasive diagnostic method for studying arterial disease.Proc. Natl. Acad. Sci.,67, 935–942.CrossRefGoogle Scholar
  23. Lees, R. S. andMyers, G. S. (1982) Noninvasive diagnosis of arterial disease.Adv. Intern.,23, 475–509.Google Scholar
  24. Padmanabhan, V., Fisher, R., Semmlow, J. L., Welkowitz, W. andKostis, J. (1989) High sensitivity PCG transducer for extended frequency applications.Proc. IEEE Frontiers in Medicine, Seattle, 57–59.Google Scholar
  25. Sangster, J. F. andOakley, C. M. (1973) Diastolic murmur of coronary artery stenosis.Br. Heart J.,35, 840–844.Google Scholar
  26. Semmlow, J. L., Welkowitz, W., Kostis, J., Mackenzie, J. W. (1983) Coronary artery disease-correlates between diastolic auditory characteristic and coronary artery stenoses.IEEE Trans. Biomed. Eng.,BME-30, 136–139.Google Scholar
  27. Semmlow, J. L., Akay, M. andWelkowitz, W. (1990) Noninvasive detection of CAD using parametric analysis methods.IEEE, EMBS Magazine,9, 33–37.CrossRefGoogle Scholar

Copyright information

© IFMBE 1991

Authors and Affiliations

  • M. Akay
    • 1
  • W. Welkowitz
    • 1
    • 2
  • J. L. Semmlow
    • 1
    • 2
  • J. Kostis
    • 1
    • 3
  1. 1.Biomedical Engineering DepartmentRutgers UniversityUSA
  2. 2.Department of Surgery (Bioengineering)UMDNJ-Robert Wood Johnson Medical SchoolUSA
  3. 3.Department of MedicineUMDNJ-Robert Wood Johnson Medical SchoolUSA

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