Author: John S Chorba; Avi M Shapiro; Le Le; John Maidens; John Prince; Steve Pham; Mia M Kanzawa; Daniel N Barbosa; Brent E White; Jason Paek; Sophie G Fuller; Grant W Stalker; Sara A Bravo; Dina Jean; Subramaniam Venkatraman; Patrick M McCarthy; James D Thomas
Title: A Deep Learning Algorithm for Automated Cardiac Murmur Detection Via a Digital Stethoscope Platform Document date: 2020_4_3
ID: fogzjrk2_8
Snippet: The end-to-end algorithm makes a sequence of binary decisions to produce one of three possible outputs. First, it determines whether the recording is of sufficient signal quality to classify as murmur/no murmur, using the output from the neural network corresponding to 'Poor Signal' as a measure of signal quality. If the signal quality is found to be below a pre-specified threshold, then the recording is classified as 'Poor Signal'. Otherwise, th.....
Document: The end-to-end algorithm makes a sequence of binary decisions to produce one of three possible outputs. First, it determines whether the recording is of sufficient signal quality to classify as murmur/no murmur, using the output from the neural network corresponding to 'Poor Signal' as a measure of signal quality. If the signal quality is found to be below a pre-specified threshold, then the recording is classified as 'Poor Signal'. Otherwise, the classifier then determines whether the signal shows the presence of a 'Heart Murmur' or can be classified as 'No Heart Murmur' based on another set threshold. All model parameters and thresholds are fixed at training time.
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