Fiorillo Luigi, Wand Michael, Marino Italo, Favaro Paolo, Faraci Francesca D
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:3509-3512. doi: 10.1109/EMBC44109.2020.9176356.
The present study evaluates how effectively a deep learning based sleep scoring system does encode the temporal dependency from raw polysomnography signals. An exhaustive range of neural networks, including state of the art architecture, have been used in the evaluation. The architectures have been assessed using a single-channel EEG Fpz-Cz from the open source Sleep-EDF expanded database. The best performing model reached an overall accuracy of 85.2% and a Cohen's kappa of 0.8, with an F1-score of stage N1 equal to 50.2%. We have introduced a new metric, δ, to better evaluate temporal dependencies. A simple feed forward architecture not only achieves comparable performance to most up-to-date complex architectures, but also does better encode the continuous temporal characteristics of sleep.Clinical relevance - A better understanding of the capability of the network in encoding sleep temporal patterns could lead to improve the automatic sleep scoring.
本研究评估了基于深度学习的睡眠评分系统对原始多导睡眠图信号的时间依赖性进行编码的有效性。评估中使用了包括最先进架构在内的一系列神经网络。这些架构是使用开源睡眠扩展数据库中的单通道脑电图Fpz-Cz进行评估的。表现最佳的模型总体准确率达到85.2%，科恩kappa系数为0.8，N1期的F1分数为50.2%。我们引入了一个新指标δ，以更好地评估时间依赖性。一种简单的前馈架构不仅能达到与大多数最新复杂架构相当的性能，而且能更好地对睡眠的连续时间特征进行编码。临床意义——更好地理解网络对睡眠时间模式的编码能力可能会改善自动睡眠评分。
