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 11 th, 2004

2. Introduction Children currently screened for heart murmurs by ear Children currently screened for heart murmurs by ear Clinical auscultation is a dying art Clinical auscultation is a dying art Clinical screening not available in many parts of the world Clinical screening not available in many parts of the world Echocardiogram is very effective in heart defect detection Echocardiogram is very effective in heart defect detection Expensive (~$1000) Expensive (~$1000) Want to create an inexpensive, effective screening process for pediatric patients through automated cardiac auscultation 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 Heart sound samples from Dr. Curt DeGroff at Children’s Hospital Cardionics stethoscope Cardionics stethoscope Matlab and CUANN software Matlab and CUANN software

4. Methodology: Signal Processing and ANN Classification

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

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