In this paper we consider the use of a well-known statistical method, namely Maximum-Likelihood Detection (MLD), to early diagnose, through a wire-free low-cost video processing-based approach, the presence of neonatal clonic seizures. Since clonic seizures are characterized by periodic movements of parts of the body (e.g., hands, legs), by evaluating the periodicity of the extracted signals it is possible to detect the presence of a clonic seizure. The proposed approach allows to differentiate clonic seizure-related movements from random ones. While we first consider a single-camera scenario, we then extend our approach to encompass the use of multiple sensors, such as several cameras or the Microsoft Kinect RBG-Depth sensor. In these cases, data fusion principles are considered to aggregate signals from multiple sensors.

Maximum-likelihood detection of neonatal clonic seizures by video image processing / Cattani, Luca; Kouamou, G. M.; Lofino, F.; Ferrari, Gianluigi; Raheli, Riccardo; Pisani, Francesco. - (2014), pp. 1-5. (Intervento presentato al convegno International Symposium on Medical ICT (ISMICT'14) tenutosi a Florence, Italy nel April) [10.1109/ISMICT.2014.6825219].

Maximum-likelihood detection of neonatal clonic seizures by video image processing

CATTANI, Luca;FERRARI, Gianluigi;RAHELI, Riccardo;PISANI, Francesco
2014-01-01

Abstract

In this paper we consider the use of a well-known statistical method, namely Maximum-Likelihood Detection (MLD), to early diagnose, through a wire-free low-cost video processing-based approach, the presence of neonatal clonic seizures. Since clonic seizures are characterized by periodic movements of parts of the body (e.g., hands, legs), by evaluating the periodicity of the extracted signals it is possible to detect the presence of a clonic seizure. The proposed approach allows to differentiate clonic seizure-related movements from random ones. While we first consider a single-camera scenario, we then extend our approach to encompass the use of multiple sensors, such as several cameras or the Microsoft Kinect RBG-Depth sensor. In these cases, data fusion principles are considered to aggregate signals from multiple sensors.
2014
Maximum-likelihood detection of neonatal clonic seizures by video image processing / Cattani, Luca; Kouamou, G. M.; Lofino, F.; Ferrari, Gianluigi; Raheli, Riccardo; Pisani, Francesco. - (2014), pp. 1-5. (Intervento presentato al convegno International Symposium on Medical ICT (ISMICT'14) tenutosi a Florence, Italy nel April) [10.1109/ISMICT.2014.6825219].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2734306
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 13
  • ???jsp.display-item.citation.isi??? 0
social impact