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

Jukka Ranta; Manu Airaksinen; Turkka Kirjavainen; Sampsa Vanhatalo; Nathan J Stevenson

doi:10.3389/fnins.2020.602852

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

Front Neurosci. 2021 Jan 14:14:602852. doi: 10.3389/fnins.2020.602852. eCollection 2020.

Authors

Jukka Ranta^{1

2}, Manu Airaksinen^{1

2}, Turkka Kirjavainen³, Sampsa Vanhatalo^{1

4}, Nathan J Stevenson⁵

Affiliations

¹ Department of Clinical Neurophysiology, BABA Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
² Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland.
³ Department of Paediatrics, Children's Hospital Helsinki University Hospital, Helsinki, Finland.
⁴ Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
⁵ Brain Modeling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.

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 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%).

Conclusion: Automated non-invasive tracking of sleep state cycling is technically feasible using measurements from a piezo element situated under a bed mattress.

Significance: An open source infant deep sleep detector of this kind allows quantitative, continuous bedside assessment of infant's sleep cycling.

Keywords: NICU; bed mattress sensor; infant sleep; intensive care monitoring; non-invasive monitoring; sleep-wake cycling.