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

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http://hdl.handle.net/10138/328059

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Ranta , J , Airaksinen , M , Kirjavainen , T , Vanhatalo , S & Stevenson , N J 2021 , ' An Open Source Classifier for Bed Mattress Signal in Infant Sleep Monitoring ' , Frontiers in Neuroscience , vol. 14 , 602852 . https://doi.org/10.3389/fnins.2020.602852

Title: An Open Source Classifier for Bed Mattress Signal in Infant Sleep Monitoring
Author: Ranta, Jukka; Airaksinen, Manu; Kirjavainen, Turkka; Vanhatalo, Sampsa; Stevenson, Nathan J.
Contributor: University of Helsinki, Children's Hospital
University of Helsinki, University of Helsinki
University of Helsinki, HUS Children and Adolescents
University of Helsinki, HUS Medical Imaging Center
Date: 2021-01-14
Language: eng
Number of pages: 11
Belongs to series: Frontiers in Neuroscience
ISSN: 1662-453X
URI: http://hdl.handle.net/10138/328059
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.
Subject: infant sleep
non-invasive monitoring
intensive care monitoring
NICU
bed mattress sensor
sleep-wake cycling
AMERICAN ACADEMY
STATES
AMPLITUDE
WAKEFULNESS
MEDICINE
VALIDATION
CRITERIA
STAGE
EMFI
3112 Neurosciences
3124 Neurology and psychiatry
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