Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America.
Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, United States of America.
Physiol Meas. 2024 Aug 6;45(8). doi: 10.1088/1361-6579/ad66aa.
Sleep spindles contain crucial brain dynamics information. We introduce the novel non-linear time-frequency (TF) 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).ConceFT effectively reduces stochastic EEG influence, enhancing spindle visibility in the TF representation. Our automated spindle detection algorithm, ConceFT-Spindle (ConceFT-S), is compared to A7 (non-deep learning) and SUMO (deep learning) using Dream and Montreal Archive of Sleep Studies (MASS) benchmark databases. We also quantify spindle IF dynamics.ConceFT-S achieves F1 scores of 0.765 in Dream and 0.791 in MASS, which surpass A7 and SUMO. We reveal that spindle IF is generally nonlinear.ConceFT offers an accurate, interpretable EEG-based sleep spindle detection algorithm and enables spindle IF quantification.
睡眠纺锤波包含关键的大脑动力学信息。我们引入了新颖的非线性时频 (TF) 分析工具“频率和时间的集中” (ConceFT)，以创建一种可解释的自动化算法，用于 EEG 数据中的睡眠纺锤波注释，并测量纺锤波瞬时频率 (IF)。ConceFT 有效地减少了随机 EEG 的影响，增强了 TF 表示中的纺锤波可见性。我们的自动纺锤波检测算法 ConceFT-Spindle (ConceFT-S) 使用 Dream 和 Montreal Archive of Sleep Studies (MASS) 基准数据库与 A7 (非深度学习) 和 SUMO (深度学习) 进行了比较。我们还量化了纺锤波 IF 的动力学。ConceFT-S 在 Dream 中的 F1 评分为 0.765，在 MASS 中的 F1 评分为 0.791，超过了 A7 和 SUMO。我们揭示了纺锤波 IF 通常是非线性的。ConceFT 提供了一种准确、可解释的基于 EEG 的睡眠纺锤波检测算法，并能够量化纺锤波 IF。
