Mendonça Fábio, Mostafa Sheikh Shanawaz, Freitas Diogo, Morgado-Dias Fernando, Ravelo-García Antonio G
Higher School of Technology and Management, University of Madeira, 9000-082 Funchal, Portugal.
Interactive Technologies Institute (ARDITI/ITI/LARSyS), 9020-105 Funchal, Portugal.
Entropy (Basel). 2022 May 13;24(5):688. doi: 10.3390/e24050688.
Methodologies for automatic non-rapid eye movement and cyclic alternating pattern analysis were proposed to examine the signal from one electroencephalogram monopolar derivation for the A phase, cyclic alternating pattern cycles, and cyclic alternating pattern rate assessments. A population composed of subjects free of neurological disorders and subjects diagnosed with sleep-disordered breathing was studied. Parallel classifications were performed for non-rapid eye movement and A phase estimations, examining a one-dimension convolutional neural network (fed with the electroencephalogram signal), a long short-term memory (fed with the electroencephalogram signal or with proposed features), and a feed-forward neural network (fed with proposed features), along with a finite state machine for the cyclic alternating pattern cycle scoring. Two hyper-parameter tuning algorithms were developed to optimize the classifiers. The model with long short-term memory fed with proposed features was found to be the best, with accuracy and area under the receiver operating characteristic curve of 83% and 0.88, respectively, for the A phase classification, while for the non-rapid eye movement estimation, the results were 88% and 0.95, respectively. The cyclic alternating pattern cycle classification accuracy was 79% for the same model, while the cyclic alternating pattern rate percentage error was 22%.
提出了自动非快速眼动和周期性交替模式分析方法，用于检查来自脑电图单极导联的信号，以评估A期、周期性交替模式周期和周期性交替模式率。研究对象包括无神经系统疾病的受试者和被诊断为睡眠呼吸障碍的受试者。对非快速眼动和A期估计进行了并行分类，研究了一维卷积神经网络（输入脑电图信号）、长短期记忆网络（输入脑电图信号或提出的特征）和前馈神经网络（输入提出的特征），以及用于周期性交替模式周期评分的有限状态机。开发了两种超参数调整算法来优化分类器。发现输入提出的特征的长短期记忆网络模型最佳，对于A期分类，准确率和受试者工作特征曲线下面积分别为83%和0.88，而对于非快速眼动估计，结果分别为88%和0.95。同一模型的周期性交替模式周期分类准确率为79%，而周期性交替模式率百分比误差为22%。
