Exploring Remote Photoplethysmography for Neonatal Pain Detection from Facial Videos
This work addresses the need for objective, non-contact pain assessment in neonates, a vulnerable population in intensive care, but the approach is incremental as it applies existing rPPG techniques to a new domain.
The paper introduces a non-contact method using remote photoplethysmography (rPPG) to estimate pulse signals from facial videos for neonatal pain detection, incorporating quality and signal-to-noise ratio parameters to improve signal extraction. Results show rPPG signals from the blue channel are most effective, and combining rPPG with audio features outperforms individual modalities.
Unaddressed pain in neonates can lead to adverse effects, including delayed development and slower weight gain, emphasising the need for more objective and reliable pain assessment methods. Hence, automated methods using behavioural and physiological pain indicators have been developed to aid healthcare professionals in the Neonatal ICU. Traditional contact-based methods for physiological parameter estimation are unsuitable for long-term monitoring and increase the risk of spreading diseases like COVID-19. We introduce a novel approach using remote photoplethysmography (rPPG) to estimate pulse signals in a non-contact manner and employ them for neonatal pain detection. The temporal signals acquired from regions-of-interest (ROIs) affected by skin deformations may exhibit lower quality and provide erroneous rPPG signals. Therefore, we incorporated a quality parameter to select the temporal signals obtained from ROIs that are least affected by skin deformations. Further, we employed signal-to-noise ratio as a fitness parameter to extract the rPPG signal corresponding to the clip that is least affected by noise. Experimental findings demonstrate that the rPPG signals provide useful information for neonatal pain detection, and signals extracted from the blue colour channel outperform those extracted from other colour channels. We also show that combining rPPG and audio features provides better results than individual modalities.