Objective: Sleep spindles contain crucial brain dynamics information. We introduce the novel non-linear time-frequency analysis tool 'Concentration of Frequency and Time' (ConceFT) to create an interpretable automated algorithm for sleep spindle annotation in EEG data and to measure spindle instantaneous frequencies (IFs). Methods: ConceFT effectively reduces stochastic EEG influence, enhancing spindle visibility in the time-frequency representation. Our automated spindle detection algorithm, ConceFT-Spindle (ConceFT-S), is compared to A7 (non-deep learning) and SUMO (deep learning) using Dream and MASS benchmark databases. We also quantify spindle IF dynamics. Results: ConceFT-S achieves F1 scores of 0.749 in Dream and 0.786 in MASS, which is equivalent to or surpass A7 and SUMO with statistical significance. We reveal that spindle IF is generally nonlinear. Conclusion: ConceFT offers an accurate, interpretable EEG-based sleep spindle detection algorithm and enables spindle IF quantification.

翻译：目的：睡眠纺锤波包含关键的脑动力学信息。我们引入新型非线性时频分析工具“频率与时间集中”（ConceFT），构建可解释的自动化脑电图睡眠纺锤波标注算法，并测量纺锤波瞬时频率（IF）。方法：ConceFT有效降低脑电图随机噪声影响，增强时频表征中纺锤波的可见性。本自动化纺锤波检测算法ConceFT-Spindle（ConceFT-S）与A7（非深度学习）及SUMO（深度学习）方法在Dream和MASS基准数据库上进行对比。同时量化纺锤波IF动态特征。结果：ConceFT-S在Dream和MASS数据库上分别取得0.749和0.786的F1分数，统计显著性地等同或超越A7与SUMO方法。研究揭示纺锤波IF普遍具有非线性特征。结论：ConceFT提供了基于脑电图的准确且可解释的睡眠纺锤波检测算法，并实现纺锤波IF的量化分析。