Spontaneous startle represents a complex motor pattern, consisting of sudden and jerky movements, which typically occurs during quiet sleep in fullterm and preterm neonates. It has been studied as an endogenous behavior by focusing on its potential contribution to an early diagnosis of central nervous system (CNS) dysfunctions. This paper aims to develop and validate an automated and non-invasive method for inferring spontaneous startles in preterm neonates. Such inference relies on measurements gathered via a hypo-allergenic pad containing a matrix of networked sensors able to measure pressure over time. The measurements gathered by sensors are processed to determine spatiotemporal features allowing to infer spontaneous startles and discriminate them from other behaviors, as well as to identify anomalies and atypical patterns possibly related to CNS issues. Preliminary results based on measurements will be presented, showing the potential benefits of the proposed method in spontaneous startle recognition.

Clinical analysis of spontaneous startles in preterm neonates via sensor networks

CONTI, Andrea
Secondo
;
BARTOLETTI, Stefania;MENIN, Damiano;SINERI, Giovanna;Domenicali, Cecilia;BALLARDINI, Elisa;BORGNA, Caterina;DONDI, Marco
Ultimo
2016

Abstract

Spontaneous startle represents a complex motor pattern, consisting of sudden and jerky movements, which typically occurs during quiet sleep in fullterm and preterm neonates. It has been studied as an endogenous behavior by focusing on its potential contribution to an early diagnosis of central nervous system (CNS) dysfunctions. This paper aims to develop and validate an automated and non-invasive method for inferring spontaneous startles in preterm neonates. Such inference relies on measurements gathered via a hypo-allergenic pad containing a matrix of networked sensors able to measure pressure over time. The measurements gathered by sensors are processed to determine spatiotemporal features allowing to infer spontaneous startles and discriminate them from other behaviors, as well as to identify anomalies and atypical patterns possibly related to CNS issues. Preliminary results based on measurements will be presented, showing the potential benefits of the proposed method in spontaneous startle recognition.
978-1-4673-9172-6
Central nervous system, preterm neonates, sensor network, spontaneous startle, statistical inference, Signal Processing, Biomedical Engineering, Instrumentation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2364801
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