Auditory property-based features and artificial neural network classifiers for the automatic detection of low-intensity snoring/breathing episodes

HIGHLIGHTS

• who: Kenji Hamabe et al. from the Graduate School of Advanced Technology and Science, Tokushima University, Tokushima, Japan have published the research work: Auditory Property-Based Features and Artificial Neural Network Classifiers for the Automatic Detection of Low-Intensity Snoring/Breathing Episodes, in the Journal: (JOURNAL) of 13/01/2022
• what: To accomplish this the authors propose in this study a new method to detect SBEs based on neural activity pattern (NAP)-based cepstral coefficients (NAPCC) and ANN classifiers. The aim of the study was to develop a more efficient method to detect lowintensity SBEs . . .
  

   

  

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