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Early cerebral palsy risk screening through wearable accelerometry in infants under nine weeks
Jimena Alvarado1,2,3, Gabriela Moreno1, Francisca Arancibia1
1Neurorehabilitation and Motor Control Lab, Department of Neuroscience, Faculty of Medicine, Universidad de Chile, 8380453, Santiago, Chile.
Insights
This study used accelerometers to identify infants at risk for cerebral palsy (CP) with 100% accuracy. This technology offers a scalable, cost-effective screening tool for early detection of CP risk factors.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Developmental Pediatrics
Background:
- Prechtl's General Movement Assessment (GMA) is crucial for early cerebral palsy (CP) detection in infants.
- Specialist access for GMA is limited, especially in low-resource settings.
- Objective: Identify optimal accelerometric features to distinguish infants with CP risk factors (RF) from healthy controls (HC).
Purpose of the Study:
- To determine effective accelerometric features for differentiating spontaneous movements in infants under 9 weeks.
- To develop a scalable and accessible screening method for neonatal neurodevelopmental disorders.
Main Methods:
- Cross-sectional study involving 48 infants (<9 weeks): 12 RF and 36 HC.
- Used instrumented assessment with limb and trunk accelerometers alongside Prechtl's method.
- Analyzed 62 accelerometer parameters and clinical variables using Random Forest Classifier.
Main Results:
- Identified 46 differentiating parameters between RF and HC groups.
- Random Forest achieved 100% classification accuracy.
- Eight optimal parameters identified, with half originating from trunk sensors.
Conclusions:
- Wireless accelerometry effectively detects infant movement patterns associated with CP risk factors.
- This method provides a scalable, personnel-independent screening solution, particularly valuable where specialized expertise is scarce.
- Enables widespread, cost-effective early risk stratification for CP.
Abstract:
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.
