By Topic

Video processing-based detection of neonatal seizures by trajectory features clustering

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Ntonfo, G.M.K. ; Dept. of Inf. Eng., Univ. of Parma, Parma, Italy ; Lofino, F. ; Ferrari, G. ; Raheli, R.
more authors

In this paper, we present a novel approach to early diagnosis, through a video processing-based approach, of the presence of neonatal seizures. In particular, image processing and gesture recognition techniques are first used to characterize typical gestures of neonatal seizures. More precisely, gesture trajectories are characterized by extracting some relevant features. In particular, selecting the point with the maximum amplitude of the optical flow vector of the video frame sequence, during a newborn movement, is selected and then tracked through an algorithm based on template matching and optical flow. The observed features are then clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The proposed approach allows to efficiently differentiate pathological repetitive movements (e.g., clonic and subtle seizures) from random ones.

Published in:

Communications (ICC), 2012 IEEE International Conference on

Date of Conference:

10-15 June 2012