Automated Deep Learning Approach for Post-Operative Neonatal Pain Detection and Prediction through Physiological Signals

Abstract

It is well-known that severe pain and powerful pain medications cause short- and long-term damage to the developing nervous system of newborns. Caregivers routinely use physiological vital signs [Heart Rate (HR), Respiration Rate (RR), Oxygen Saturation (SR)] to monitor post-surgical pain in the Neonatal Intensive Care Unit (NICU). Here we present a novel approach that combines continuous, non-invasive monitoring of these vital signs and Computer Vision/Deep Learning to make automatic neonate pain detection with an accuracy of 74% AUC, 67.59% mAP. Further, we report for the first time our Early Pain Detection (EPD) approach that explores prediction of the time to onset of post-surgical pain in neonates. Our EPD can alert NICU workers to postoperative neonatal pain about 5 to 10 minutes prior to pain onset. In addition to alleviating the need for intermittent pain assessments by busy NICU nurses via long-term observation, our EPD approach creates a time window prior to pain onset for the use of less harmful pain mitigation strategies. Through effective pain mitigation prior to spinal sensitization, EPD could minimize or eliminate severe post-surgical pain and the consequential need for powerful analgesics in post-surgical neonates.

Keywords: deep learning; neonatal pain; neural networks; pain prediction; vital signs.

Fig. 1.

This figure shows the overall process for selection input and labels used by the model for both detection and prediction.

Fig. 2.

Pain Detection Result Figures.

Fig. 3.

Experimental Results for 5 min α. Left to right: ROC curve, Confusion Matrix (in percentages, %); Left to Right: ||v|| in 1 min length, 2 min length, 5 min length.

Fig. 4.

Experimental Results for 10 min α. Left to right: ROC curve, Confusion Matrix (in percentages, %); Left to Right: ||v|| in 1 min length, 2 min length, 5 min length