Automated general movement assessment for perinatal stroke screening in infants

Perinatal stroke (PS) is a serious condition that, if undetected and thus untreated, often leads to lifelong disability, in particular, Cerebral Palsy (CP). 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 developing PS.

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 PS screening system that can be used population-wide.

Our method is straightforward to apply, inexpensive, and reliable with regards to the accuracy of analysis results.

Although cranial imaging was used to provide a definitive gold standard by which to classify the infants as having had Perinatal stroke or not, definitive imaging with MRI is costly (around £1,000 per scan for infants in the UK), and requires sedation or general anaesthetic in some cases, which has an associated risk.

Due to the importance of early, reliable recognition of indications of PS we ultimately aim for population-wide screening.