[1M4-GS-13] AI application: Human detection

[1M4-GS-13-01] Development of Sleep Spindle Detection Method by Combining Wavelet Synchrosqueezed Transform and RUSBoost

〇Koichi Fujiwara Fujiwara¹, Takafumi Kinoshita², Yukiyoshi Sumi³, Masahiro Matsuo³, Keiko Ogawa⁴, Manabu Kano², Hiroshi Kadotani³ (1. Nagoya University, 2. Kyoto University, 3. Shiga University of Medical Science, 4. Hiroshima University)

Keywords:EEG analysis, Spindle detection, Wavelet synchrosqueezed transform, RUSBoost, Sleep medicine

Sleep spindles are important electroencephalographic (EEG) waveforms in sleep medicine; however, it is burdensome even for experts to detect spindles, so automatic spindle detection methodologies have been investigated. Conventional methods utilize waveforms template matching or machine learning for detecting spindles. In the former approach, it is necessary to tune thresholds for individual adaptation, while the latter approach has the problem of imbalanced data because the amount of sleep spindles is small compared with the entire EEG data. The present work proposes a sleep spindle detection method that combines wavelet synchrosqueezed transform (SST) and random under-sampling boosting (RUSBoost). SST is a time-frequency analysis method suitable for extracting features of spindle waveforms. RUSBoost is a framework for coping with the imbalanced data problem. The proposed SST-RUS can deal with the imbalanced data in spindle detection and does not require threshold tuning because RUSBoost uses majority voting of weak classifiers for discrimination. The performance of SST-RUS was validated using an open-access database called the Montreal archives of sleep studies cohort 1 (MASS-C1), which showed an F-measure of 0.70 with a sensitivity of 76.9% and a positive predictive value of 61.2%. The proposed method can reduce the burden of PSG scoring.