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Enhanced automated sleep spindle detection algorithm based on synchrosqueezing

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Abstract

Detection of sleep spindles is of major importance in the field of sleep research. However, manual scoring of spindles on prolonged recordings is very laborious and time-consuming. In this paper, we introduce a new algorithm based on synchrosqueezing transform for detection of sleep spindles. Synchrosqueezing is a powerful time–frequency analysis tool that provides precise frequency representation of a multicomponent signal through mode decomposition. Subsequently, the proposed algorithm extracts and compares the basic features of a spindle-like activity with its surrounding, thus adapting to an expert’s visual criteria for spindle scoring. The performance of the algorithm was assessed against the spindle scoring of one expert on continuous electroencephalogram sleep recordings from two subjects. Through appropriate choice of synchrosqueezing parameters, our proposed algorithm obtained a maximum sensitivity of 96.5 % with 98.1 % specificity. Compared to previously published works, our algorithm has shown improved performance by enhancing the quality of sleep spindle detection.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Authors and Affiliations

  1. Knight Cardiovascular Institute, Oregon Health and Science University, Portland, OR, 97239, USA

    Muammar M. Kabir

  2. Department of Mechanical Engineering, Texas A&M at Qatar, Doha, Qatar

    Reza Tafreshi

  3. Douglas Hospital Research Center, Montreal, Canada

    Diane B. Boivin

  4. Weill Cornell Medical College in Qatar, Doha, Qatar

    Naim Haddad

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  1. Muammar M. Kabir
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  2. Reza Tafreshi
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Correspondence to Muammar M. Kabir.

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Kabir, M.M., Tafreshi, R., Boivin, D.B. et al. Enhanced automated sleep spindle detection algorithm based on synchrosqueezing. Med Biol Eng Comput 53, 635–644 (2015). https://doi.org/10.1007/s11517-015-1265-z

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  • DOI: https://doi.org/10.1007/s11517-015-1265-z

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