Selected article for: "prior model and probability distribution"
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_7
    Snippet: To complete the training set for a supervised learning problem, audio recordings and phonocardiograms (PCGs) were reviewed and labeled by one physician as one of three classes: no heart murmur, heart murmur, or poor signal. Recordings of lung sounds, noise, human speech, etc. were examples of data labelled poor signal. The neural network model used for PCG classification uses a ResNet 21 deep convolutional neural network architecture. Prior to be.....
    Document: To complete the training set for a supervised learning problem, audio recordings and phonocardiograms (PCGs) were reviewed and labeled by one physician as one of three classes: no heart murmur, heart murmur, or poor signal. Recordings of lung sounds, noise, human speech, etc. were examples of data labelled poor signal. The neural network model used for PCG classification uses a ResNet 21 deep convolutional neural network architecture. Prior to being sent to the model, input recordings are filtered using an 8 th order Butterworth high-pass filter at 30 Hertz and downsampled to 2000 Hertz. The final output of the network is a three component probability distribution used to classify the recording.

    Search related documents:
    Co phrase search for related documents
    • deep convolutional neural network architecture and learning problem: 1, 2
    • deep convolutional neural network architecture and network model: 1, 2, 3, 4, 5
    • deep convolutional neural network architecture and neural network: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18
    • deep convolutional neural network architecture and neural network architecture: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18