Ranta Jukka, Airaksinen Manu, Kirjavainen Turkka, Vanhatalo Sampsa, Stevenson Nathan J
Department of Clinical Neurophysiology, BABA Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland.
Front Neurosci. 2021 Jan 14;14:602852. doi: 10.3389/fnins.2020.602852. eCollection 2020.
To develop a non-invasive and clinically practical method for a long-term monitoring of infant sleep cycling in the intensive care unit.
Forty three infant polysomnography recordings were performed at 1-18 weeks of age, including a piezo element bed mattress sensor to record respiratory and gross-body movements. The hypnogram scored from polysomnography signals was used as the ground truth in training sleep classifiers based on 20,022 epochs of movement and/or electrocardiography signals. Three classifier designs were evaluated in the detection of deep sleep (N3 state): support vector machine (SVM), Long Short-Term Memory neural network, and convolutional neural network (CNN).
Deep sleep was accurately identified from other states with all classifier variants. The SVM classifier based on a combination of movement and electrocardiography features had the highest performance (AUC 97.6%). A SVM classifier based on only movement features had comparable accuracy (AUC 95.0%). The feature-independent CNN resulted in roughly comparable accuracy (AUC 93.3%).
Automated non-invasive tracking of sleep state cycling is technically feasible using measurements from a piezo element situated under a bed mattress.
An open source infant deep sleep detector of this kind allows quantitative, continuous bedside assessment of infant's sleep cycling.
开发一种用于重症监护病房长期监测婴儿睡眠周期的非侵入性且临床实用的方法。
对43名1至18周龄婴儿进行多导睡眠图记录，其中包括一个压电元件床垫传感器，用于记录呼吸和全身运动。基于20,022个运动和/或心电图信号时段，将从多导睡眠图信号中评分得到的睡眠图用作训练睡眠分类器的基本事实。在深度睡眠（N3状态）检测中评估了三种分类器设计：支持向量机（SVM）、长短期记忆神经网络和卷积神经网络（CNN）。
所有分类器变体均能准确地将深度睡眠与其他状态区分开来。基于运动和心电图特征组合的支持向量机分类器性能最高（曲线下面积为97.6%）。仅基于运动特征的支持向量机分类器具有相当的准确性（曲线下面积为95.0%）。与特征无关的卷积神经网络的准确性大致相当（曲线下面积为93.3%）。
利用位于床垫下方的压电元件进行测量，自动非侵入性跟踪睡眠状态周期在技术上是可行的。
这种开源的婴儿深度睡眠探测器能够在床边对婴儿的睡眠周期进行定量、连续评估。
