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

    4 Citations (Scopus)
    73 Downloads (Pure)


    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
    Publication statusPublished - 14 Jan 2021
    MoE publication typeA1 Journal article-refereed


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


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