Detection of cardiac pathology: time intervals and spectral analysis.

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.

Atrial septal defect: a diagnostic approach.

A simple objective screening method for diagnosis of the atrial septal defect (ASD) is needed. Acoustic signals were collected from 61 children with ASD and 60 with a physiological murmur. The second heart sound (S2) and the spectrum of systolic murmur were analysed. A statistical model was designed using stepwise logistic regression analysis. Significant variables distinguishing pathological form normal findings were the interval between the first heart sound and the beginning of systolic murmur or the respiratory variation of S2, and the frequency of the murmur at its maximum intensity. The area under the ROC curve was 0.922; indicating very good fit of the model and the confidence interval was 0.872-0.971. The sensitivity of the model was 91% and the specificity 73%. The analysis of acoustic findings from the heart is a valuable tool in diagnosing ASD. The next step will be automating this process.