Automatic classification of systolic heart murmurs

This paper describes a system for discriminating innocent from pathologic systolic heart murmurs in children based on auscultation recordings. For sound signal analysis the use of reassigned spectrogram is suggested. Both dimensions and noise of the time-frequency representation were significantly reduced using higher order singular value decomposition. Optimal dimensions were selected through cross-validation experiments on a database of auscultation recordings with systolic murmurs from the University Hospital of Heraklion. The database only consisted with recordings of high misclassification rate by general practitioners. Using support vector machines for classification, the suggested approach achieved an Equal Error Rate of 6.71 ± 1.18% and an Area Under the Curve score of 0.9758 ± 0.0053 (95% confidence intervals). The performance of the suggested classification system is comparable to the reported accuracy of experienced pediatric cardiologists on the same database, while it outperforms alternative signal representations based on simple STFT schemes.