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Detection of cardiac pathology: time intervals and spectral analysis.

På svenska

Author

  • Milad El-Segaier
  • Erkki Pesonen
  • Sakari Lukkarinen
  • Kristoffer Peters
  • Leif Sörnmo
  • Raimo Sepponen

Summary, in English

AIM: To develop an objective diagnostic method that facilitates detection of noncyanotic congenital heart diseases. METHODS: Heart sounds and murmurs were recorded from 60 healthy children and 173 children with noncyanotic congenital heart disease. Time intervals were measured and spectrum of the systolic murmurs analyzed. Stepwise logistic regression analysis was used to distinguish physiological from pathological signals. The receiver operating characteristic (ROC) curve was plotted to show the classification performance of the model and the area under the curve (AUC) was calculated. The probability cut-off points for calculation of sensitivities and specificities were estimated. RESULTS: The distinguishing variables were the interval from the end of the first heart sound (S(1)) and the beginning of the systolic murmur, respiratory variation of the splitting of the second heart sound, intensity of the systolic murmur, and standard deviation of the interval from the end of the S(1) to the maximum intensity of the murmur. The AUC was 0.95, indicating an excellent classification performance of the model. The sensitivity of 95% and specificity of 72% was achieved at a probability cut-off point of 0.45. Significant cardiac defects were correctly classified. CONCLUSION: Interval measurements and spectral analysis can be used to confirm significant noncyanotic congenital heart diseases. Further development of the method is necessary to detect also insignificant heart defects.

Publishing year

2007

Language

English

Pages

1036-1042

Publication/Series

Acta Pædiatrica

Volume

96

Issue

7

Document type

Journal article

Publisher

Wiley-Blackwell

Topic

  • Pediatrics

Keywords

  • receiver operating characteristic curve
  • murmur classification
  • heart sounds
  • stepwise logistic
  • signal analysis
  • regression analysis

Status

Published

ISBN/ISSN/Other

  • ISSN: 1651-2227