A classifier based on the artificial neural network approach for cardiologic auscultation in pediatrics.
OBJECTIVE
This research work was aimed at developing a reliable screening device for diagnosis of heart murmurs in pediatrics. This is a significant problem in pediatric cardiology because of the high rate of incidence of heart murmurs in this population (reportedly 77-95%), of which only a small fraction arises from congenital heart disease. The screening devices currently available (e.g. chest X-ray, electrocardiogram, etc.) suffer from poor sensitivity and specificity in detecting congenital heart disease. Thus, patients with heart murmurs today are frequently assessed by consultation as well with advanced imaging techniques. The most prominent among these is echocardiography. However, echocardiography is expensive and is usually only available in healthcare centers in major cities. Thus, for patients being evaluated with a heart murmur, developing a more accurate screening device is vital to efforts in reducing health care costs.


METHODS
The data set was collected from incoming pediatrics at the cardiology clinic of The Children's Hospital (Denver, Colorado), on whom echocardiography had been performed to identify congenital heart disease. Recordings of approximately 10-15s duration were made at 44,100Hz and the average record length was approximately 60,000 points. The best three cycles with respect to signal quality sounds were extracted from the original recording. The resulting data comprised 241 examples, of which 88 were examples of innocent murmurs and 153 were examples of pathological murmurs. The selected phonocardiograms were subject to the digital signal processing (DSP) technique of fast Fourier transform (FFT) to extract the energy spectrum in frequency domain. The spectral range was 0-300Hz at a resolution of 1Hz. The processed signals were used to develop statistical classifiers and a classifier based on our in-house artificial neural network (ANN) software. For the latter, we also tried enhancements to the basic ANN scheme. These included a method for setting the decision-threshold and a scheme for consensus-based decision by a committee of experts.


RESULTS
Of the different classifiers tested, the ANN-based classifier performed the best. With this classifier, we were able to achieve classification accuracy of 83% sensitivity and 90% specificity in discriminating between innocent and pathological heart murmurs. For the problem of discrimination between innocent murmurs and murmurs of the ventricular septal defect (VSD), the accuracy was higher, with sensitivity of 90% and specificity of 93%.


CONCLUSIONS
An ANN-based approach for detection and identification of congenital heart disease in pediatrics from heart murmurs can result in an accurate screening device. Considering that only a simple feature set was used for classification, the results are very encouraging and point out the need for further development using improved feature set with more potent diagnostic variables.
Bhatikar
Sanjay R
SR
Department of Mechanical Engineering, University of Colorado, CB #427, Boulder, CO 80309, USA. bhatikar@ucsu.colorado.edu
DeGroff
Curt
C
Mahajan
Roop L
RL
D002648:Child; D004452:Echocardiography; D005103:Expert Systems; D005583:Fourier Analysis; D006326:Heart Auscultation / Q000145:classification*; D006330:Heart Defects, Congenital / Q000175:diagnosis / Q000000981:diagnostic imaging; D006337:Heart Murmurs / Q000145:classification* / Q000175:diagnosis; D006345:Heart Septal Defects, Ventricular / Q000175:diagnosis; D006801:Humans; D008403:Mass Screening; D016571:Neural Networks, Computer; D010701:Phonocardiography; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted