Monitoring and Diagnosing Neonatal Seizures by Video Signal Processing

In this thesis we consider the use of well-known statistical methods to early diagnose, through wire-free low-cost video processing, the potential presence of seizures. For this purpose several approaches, have been proposed: periodicity-based, classification-based and clustering-based approaches.

[1]  Gianluigi Ferrari,et al.  Extraction of video features for real-time detection of neonatal seizures , 2011, 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks.

[2]  Gianluigi Ferrari,et al.  Low-complexity image processing for real-time detection of neonatal clonic seizures , 2010, 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010).

[3]  Gianluigi Ferrari,et al.  Video processing-based detection of neonatal seizures by trajectory features clustering , 2012, 2012 IEEE International Conference on Communications (ICC).

[4]  Gianluigi Ferrari,et al.  Real-time automated detection of clonic seizures in newborns , 2014, Clinical Neurophysiology.

[5]  Gianluigi Ferrari,et al.  Maximum-likelihood detection of neonatal clonic seizures by video image processing , 2014, 2014 8th International Symposium on Medical Information and Communication Technology (ISMICT).