An Open Source Classifier for Bed Mattress Signal in Infant Sleep Monitoring.

ABSTRACT:

Objective

To develop a non-invasive and clinically practical method for a long-term monitoring of infant sleep cycling in the intensive care unit.

Methods

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).

Results

Deep sleep was accurately identified from other states with all cl

SUBMITTER: Ranta J 

PROVIDER: S-EPMC7840576 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature