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

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.