通过集中频率和时间（ConceFT）揭开睡眠纺锤体的神秘面纱。

IF 2.3 4区医学 Q3 BIOPHYSICS

Physiological measurement Pub Date : 2024-08-06 DOI:10.1088/1361-6579/ad66aa

Riki Shimizu, Hau-Tieng Wu

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引用次数: 0

摘要

目的睡眠棘波包含重要的大脑动力学信息。我们介绍了新颖的非线性时频分析工具 "频率和时间的集中"（ConceFT），以创建一种可解释的自动算法，用于在脑电图数据中标注睡眠纺锤体，并测量纺锤体的瞬时频率（IFs）：方法：ConceFT 可有效降低随机脑电图的流变性，提高主轴在时频表征中的可见度。我们的自动纺锤体检测算法 ConceFT-Spindle（ConceFT-S）使用 Dream 和 MASS 基准数据库与 A7（非深度学习）和 SUMO（深度学习）进行了比较。我们还量化了主轴中频动态。主要结果：ConceFT-S 在 Dream 和 MASS 中的 F1 分数分别为 0.765 和 0.791，超过了 A7 和 SUMO。我们发现纺锤体中频一般是非线性的：ConceFT提供了一种准确、可解释的基于脑电图的睡眠纺锤体检测算法，并能对纺锤体中频进行量化。

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Unveil sleep spindles with concentration of frequency and time (ConceFT).

Objective.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).Approach.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.Main results.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.Significance.ConceFT offers an accurate, interpretable EEG-based sleep spindle detection algorithm and enables spindle IF quantification.

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来源期刊

Physiological measurement 生物-工程：生物医学

CiteScore

5.50

自引率

9.40%

发文量

124

审稿时长

3 months

期刊介绍： Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.