Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Computers and Biomedical Research
Comparative Approaches to Medical Reasoning
Comparative Approaches to Medical Reasoning
Machine learning method for knowledge discovery experimented with otoneurological data
Computer Methods and Programs in Biomedicine
Classification of Continuous Heart Sound Signals Using the Ergodic Hidden Markov Model
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Using Kullback-Leibler Distance in Determining the Classes for the Heart Sound Signal Classification
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Support Vectors Machine-based identification of heart valve diseases using heart sounds
Computer Methods and Programs in Biomedicine
LETTER TO THE EDITOR: Clinical benchmarking for the validation of AI medical diagnostic classifiers
Artificial Intelligence in Medicine
Heart murmurs identification using random forests in assistive environments
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Artificial neural network-based model for predicting VO2max from a submaximal exercise test
Expert Systems with Applications: An International Journal
Selection of wavelet packet measures for insufficiency murmur identification
Expert Systems with Applications: An International Journal
A classification approach for the heart sound signals using hidden markov models
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
MCMC Bayesian inference for heart sounds screening in assistive environments
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Neural Network Approaches to Grade Adult Depression
Journal of Medical Systems
Expert Systems with Applications: An International Journal
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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 and material:: 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.