Automated General Movement Assessment for Perinatal Stroke Screening in Infants

Perinatal stroke (PS) is a serious condition that often leads to life-long disability, in particular cerebral palsy (CP). Early detection and early intervention could improve motor outcome. In clinical settings, Prechtl’s general movement assessment (GMA) can be used to classify infant movements using a Gestalt approach, identifying infants at high risk of abnormal motor development. Training and maintenance of assessment skills are essential and expensive for the correct use of GMA, yet many practitioners lack these skills, preventing larger-scale screening and leading to significant risks of missing affected infants. We present an automated approach to GMA, based on body-worn accelerometers and a novel sensor data analysis method—discriminative pattern discovery (DPD)—that is designed to cope with scenarios where only coarse annotations of data are available for model training. We demonstrate the effectiveness of our approach in a study with 34 newborns (21 typically developing infants and 13 PS infants with abnormal movements). Our method is able to correctly recognise the trials with abnormal movements with at least the accuracy that is required by newly trained human annotators (75%), which is encouraging towards our ultimate goal of an automated screening system that can be used population-wide.