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Identifying Heart Murmurs Through the Use of Artificial Neural Network Classifiers

Identifying Heart Murmurs Through the Use of Artificial Neural Network Classifiers. Aaron Aikin aaron.aikin@colorado.edu Supervisor: Roop Mahajan In collaboration with The Children’s Hospital in Denver. Introduction to Research, October 11 th , 2004. Introduction.

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Identifying Heart Murmurs Through the Use of Artificial Neural Network Classifiers

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  1. Identifying Heart Murmurs Through the Use of Artificial Neural Network Classifiers Aaron Aikin aaron.aikin@colorado.edu Supervisor: Roop Mahajan In collaboration with The Children’s Hospital in Denver Introduction to Research, October 11th, 2004

  2. Introduction • Children currently screened for heart murmurs by ear • Clinical auscultation is a dying art • Clinical screening not available in many parts of the world • Echocardiogram is very effective in heart defect detection • Expensive (~$1000) • Want to create an inexpensive, effective screening process for pediatric patients through automated cardiac auscultation

  3. Facilities • Heart sound samples from Dr. Curt DeGroff at Children’s Hospital • Cardionics stethoscope • Matlab and CUANN software

  4. Methodology: Signal Processing and ANN Classification

  5. Current Performance • ~75% overall performance in determining pathological from innocent heart sounds • ~10% drop when noisy data is included • Goal is >85%

  6. Research Plans / Timeline • Limited by CUANN software • Use Principle Component Analysis for data reduction (late October) • Implement code into Matlab (early November) • Limitations in time-averaged FFT • Explore wavelet analysis (early December) • Potential limitations in ANN strategy • Explore other classification techniques • Nearest neighbor approach • “Clustering” methods • By February, Improve overall accuracy

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