University of Madeira, Funchal, Portugal.
Interactive Technologies Institute (ITI/LARSyS) and M-ITI, Funchal, Portugal.
Sleep. 2023 Jan 11;46(1). doi: 10.1093/sleep/zsac217.
Sleep stability can be studied by evaluating the cyclic alternating pattern (CAP) in electroencephalogram (EEG) signals. The present study presents a novel approach for assessing sleep stability, developing an index based on the CAP A-phase characteristics to display a sleep stability profile for a whole night's sleep.
Two ensemble classifiers were developed to automatically score the signals, one for "A-phase" and the other for "non-rapid eye movement" estimation. Both were based on three one-dimension convolutional neural networks. Six different inputs were produced from the EEG signal to feed the ensembles' classifiers. A proposed heuristic-oriented search algorithm individually tuned the classifiers' structures. The outputs of the two ensembles were combined to estimate the A-phase index (API). The models can also assess the A-phase subtypes, their API, and the CAP cycles and rate.
Four dataset variations were considered, examining healthy and sleep-disordered subjects. The A-phase average estimation's accuracy, sensitivity, and specificity range was 82%-87%, 72%-80%, and 82%-88%, respectively. A similar performance was attained for the A-phase subtype's assessments, with an accuracy range of 82%-88%. Furthermore, in the examined dataset's variations, the API metric's average error varied from 0.15 to 0.25 (with a median range of 0.11-0.24). These results were attained without manually removing wake or rapid eye movement periods, leading to a methodology suitable to produce a fully automatic CAP scoring algorithm.
Metrics based on API can be understood as a new view for CAP analysis, where the goal is to produce and examine a sleep stability profile.
可以通过评估脑电图 (EEG) 信号中的循环交替模式 (CAP) 来研究睡眠稳定性。本研究提出了一种评估睡眠稳定性的新方法，开发了一种基于 CAP A 相特征的指标，以显示整个夜间睡眠的睡眠稳定性谱。
开发了两个集成分类器来自动评分信号，一个用于“A 相”，另一个用于“非快速眼动”估计。两者均基于三个一维卷积神经网络。从 EEG 信号生成六个不同的输入，以馈送集成分类器。提出了一种启发式定向搜索算法，单独调整分类器的结构。两个集成的输出组合起来估计 A 相指数 (API)。该模型还可以评估 A 相亚型、其 API 以及 CAP 周期和速率。
考虑了四种数据集变化，检查了健康和睡眠障碍的受试者。A 相平均估计的准确率、灵敏度和特异性范围分别为 82%-87%、72%-80%和 82%-88%。A 相亚型评估的性能相似，准确率范围为 82%-88%。此外，在所检查的数据集变化中，API 指标的平均误差范围为 0.15 到 0.25（中位数范围为 0.11-0.24）。这些结果是在不手动去除清醒或快速眼动期的情况下获得的，从而产生了一种适合生成完全自动 CAP 评分算法的方法。
基于 API 的指标可以被理解为 CAP 分析的一种新视角，其目的是生成和检查睡眠稳定性谱。
