IEEE Crackle And Breathing Phase Detection In Lung Sounds With Deep Bidirectional Gated Recurrent Neural Networks 2018 RETAiL eBook iDiB

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  |  IEEE.Crackle.And.Breathing.Phase.Detection.In.Lung.Sounds.With.  |
  |  .Deep.Bidirectional.Gated.Recurrent.Neural.Networks.2018.        |
  |  .RETAiL.eBook-iDiB                                               |
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         PACKS________: idib-cabwml                                   
         RELEASE DATE_: 2019.07.06                                    
         YEAR_________: 2018                                          
         LANGUAGE_____: English                                       
         RELEASE SIZE_: 2 Mb - 01 files                               
         PAGES________: 4                                             
   
         SOURCE_______: https://ieeexplore.ieee.org/document/8512237  
         PROTECTION___: AmazonDRM(removed)                            
         FORMAT_______: .pdf                                          
   
         ISBN CODE____: 978-1-5386-3646-6                             
         GENRE________: Scientific papers                             
   
         NOTES________: Enjoy                                         
   
         PLOT_________: Abstract: In this paper, we present a method  
                        for event detection in single-channel lung    
                        sound recordings. This includes the detection 
                        of crackles and breathing phase events        
                        (inspiration/expiration). Therefore, we       
                        propose an event detection approach with      
                        spectral features and bidirectional gated     
                        recurrent neural networks (BiGRNNs). In our   
                        experiments, we use multichannel lung sound   
                        recordings from lung-healthy subjects and     
                        patients diagnosed with idiopathic pulmonary  
                        fibrosis, collected within a clinical trial.  
                        We achieve an event-based F-score of F 1 ~ 86 
                        for crackles. The proposed method shows       
                        robustness regarding the contamination of the 
                        lung sound recordings with noise, bowel and   
                        heart sounds.                                 
   
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