• DocumentCode
    1796946
  • Title

    Automatic detection of inspiration related snoring signals from original audio recording

  • Author

    Kun Qian ; Zhiyong Xu ; Huijie Xu ; Boon Poh Ng

  • Author_Institution
    Sch. of Electron. & Opt. Eng., Nanjing Univ. of Sci. & Technol., Nanjing, China
  • fYear
    2014
  • fDate
    9-13 July 2014
  • Firstpage
    95
  • Lastpage
    99
  • Abstract
    Inspiration related snoring signals (IRSS) are essential for doctors and researchers to develop further study and establishment of personal health database. How to detect IRSS automatically from original audio recording is significant in methods of acoustic based Obstructive Sleep Apnea/Hypopnea Syndrome (OSAHS) diagnosis and monitoring. We proposed a systematic approach combining signal processing with machine learning techniques to detect IRSS from audio recording. Both the experimental results and computer studies demonstrate the efficiency of the proposed approach.
  • Keywords
    acoustic signal processing; audio recording; bioacoustics; diseases; learning (artificial intelligence); medical signal processing; patient diagnosis; patient monitoring; sleep; acoustic based obstructive sleep apnea-hypopnea syndrome diagnosis; acoustic based obstructive sleep apnea-hypopnea syndrome monitoring; automatic inspiration related snoring signal detection; machine learning; original audio recording; personal health database establishment; signal processing; Abstracts; Accuracy; Databases; Educational institutions; Sensors; Sleep apnea; Training; Apnea/Hypopnea Syndrome; Obstructive Sleep; inspiration related snoring signals; machine learning; signal processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal and Information Processing (ChinaSIP), 2014 IEEE China Summit & International Conference on
  • Conference_Location
    Xi´an
  • Print_ISBN
    978-1-4799-5401-8
  • Type

    conf

  • DOI
    10.1109/ChinaSIP.2014.6889209
  • Filename
    6889209