Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detection

@article{Humayun2018LearningFF,
  title={Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detection},
  author={A. Humayun and Shabnam Ghaffarzadegan and Z. Feng and T. Hasan},
  journal={2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
  year={2018},
  pages={1408-1411}
}
  • A. Humayun, Shabnam Ghaffarzadegan, +1 author T. Hasan
  • Published 2018
  • Computer Science, Medicine
  • 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • Automatic heart sound abnormality detection can play a vital role in the early diagnosis of heart diseases, particularly in low-resource settings. The state-of-the-art algorithms for this task utilize a set of Finite Impulse Response (FIR) band-pass filters as a front-end followed by a Convolutional Neural Network (CNN) model. In this work, we propound a novel CNN architecture that integrates the front-end band-pass filters within the network using time-convolution (tConv) layers, which enables… CONTINUE READING
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