Detection of snores using source separation on an Emfit signal

    Tutkimustuotos: ArtikkeliTieteellinenvertaisarvioitu

    13 Sitaatiot (Scopus)

    Abstrakti

    Snoring (SN) is an early sign of upper airway dysfunction, and it is strongly associated with obstructive sleep apnea (OSA). SN detection is important to monitor SN objectively and to improve the diagnostic sensitivity of sleep-disordered breathing (SDB). In this study, an automatic snore detection method using an Emfit (Electromechanical film transducer) signal is presented. Representative polysomnographs of normal breathing (NB) and SN periods from 30 subjects were selected. Individual SN events were identified using source separation applying nonnegative matrix factorization deconvolution (NMFD). The algorithm was evaluated using manual annotation of the polysomnographic recordings. According to our results, the sensitivity (Se), and the positive predictive value (PPV) of the developed method to reveal snoring from the Emfit signal were 82.81% and 86.29%, respectively. Compared to other approaches, our method adapts to the individual spectral snoring profile of the subject rather than matching a particular spectral profile, estimates the snoring intensity, and obtains the specific spectral profile of the snores in the epoch. Additionally, no training is necessary. This study suggests that it is possible to detect individual SN events with Emfit mattress, which can be used as a contactless alternative to more conventional methods such as piezo-snore sensors or microphones.

    AlkuperäiskieliEnglanti
    Sivut1157-1167
    JulkaisuIEEE Journal of Biomedical and Health Informatics
    Vuosikerta22
    Numero4
    DOI - pysyväislinkit
    TilaJulkaistu - 2018
    OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

    Tutkimusalat

    • Journal Article

    Julkaisufoorumi-taso

    • Jufo-taso 2

    Sormenjälki

    Sukella tutkimusaiheisiin 'Detection of snores using source separation on an Emfit signal'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

    Siteeraa tätä