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.

中文翻译：

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使用RUSBoost和同步压缩小波变换的睡眠主轴检测。

睡眠纺锤体是睡眠医学中重要的脑电图（EEG）波形。然而，即使对于专家而言，主轴的检测也很繁重，因此已经研究了自动主轴检测方法。传统方法利用波形模板匹配或机器学习来检测主轴。在前一种方法中，有必要调整用于个体适应的阈值，而在后一种方法中，由于睡眠纺锤的数量与整个EEG数据相比较小，因此存在数据不平衡的问题。本工作提出了一种结合小波同步压缩变换（SST）和随机欠采样增强（RUSBoost）的睡眠主轴检测方法。SST是一种时频分析方法，适用于提取主轴波形的特征。RUSBoost是用于解决数据不平衡问题的框架。提出的SST-RUS可以处理主轴检测中的不平衡数据，并且不需要阈值调整，因为RUSBoost使用弱分类器的多数投票进行区分。SST-RUS的性能已通过名为“睡眠研究队列1蒙特利尔档案”（MASS-C1）的开放访问数据库进行了验证，该数据库显示F值为0.70，灵敏度为76.9％，阳性预测值为61.2。 ％。所提出的方法可以减轻PSG评分的负担。SST-RUS的性能已通过名为“睡眠研究队列1蒙特利尔档案”（MASS-C1）的开放访问数据库进行了验证，该数据库显示F值为0.70，灵敏度为76.9％，阳性预测值为61.2。 ％。所提出的方法可以减轻PSG评分的负担。SST-RUS的性能已通过名为“睡眠研究队列1蒙特利尔档案”（MASS-C1）的开放访问数据库进行了验证，该数据库显示F值为0.70，灵敏度为76.9％，阳性预测值为61.2。 ％。所提出的方法可以减轻PSG评分的负担。