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

Jukka Ranta*, Manu Airaksinen, Turkka Kirjavainen, Sampsa Vanhatalo, Nathan J. Stevenson

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
23 Downloads (Pure)

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.

Original languageEnglish
Article number602852
Number of pages11
JournalFrontiers in Neuroscience
Volume14
DOIs
Publication statusPublished - 14 Jan 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • bed mattress sensor
  • infant sleep
  • intensive care monitoring
  • NICU
  • non-invasive monitoring
  • sleep-wake cycling

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