Early cerebral palsy risk screening through wearable accelerometry in infants under nine weeks.

The General Movement Assessment (GMA) according to Prechtl's method has the best predictive value for early detection of Cerebral Palsy (CP) in infants under 5 months of age. However, access to specialists in this assessment is scarce, particularly in lower and middle-income countries. The main objective was to determine the better accelerometric features for distinguishing spontaneous movements of infants with risk factors for CP (RF), and healthy controls (HC), under 9 weeks. We carried out a cross-sectional study. General movements (GMs) were recorded in 48 infants under 9 weeks of age, 12 RF and 36 HC, using an instrumented assessment (accelerometers on limbs and trunk) and Prechtl's method. Clinical variables and 62 accelerometer parameters were collected and analysed using descriptive and inferential statistics. To classify infants, we employed the Random Forest Classifier, based on their condition (Healthy vs. Risk Factors), with subsequent analysis of model accuracy. Afterward, we determined the features that best differentiated between the two groups. We found 46 parameters that differentiate RF and HC groups. Random Forest classified infants with 100% accuracy. Eight parameters were optimal for differentiation, and half of them were from trunk sensors. Wireless accelerometry effectively identified infant movement patterns indicative of cerebral palsy risk factors. This study establishes a scalable, personnel-independent screening of neonatal neurodevelopmental disorders, especially where specialized expertise is unavailable via Precht´s method. Deployment would facilitate widespread, cost-effective early risk stratification for CP.