CARI'96 : actes du 3ème colloque africain sur la recherche en informatique = CARI'96 : proceedings of the 3rd African conference on research in computer science

Cet article décrit une approche d'intervention en milieu urbain destinée à améliorer la connaissance et la gestion des paramètres caractéristiques d'un quartier à habitat spontané en vue de son aménagement. La solution que nous proposons est soutenue par la description d'un système informatique (SIAD) devant aider (sans contraintes) les décideurs dans le choix de l'action finale. (Résumé d'auteur

Extraction of video features for real-time detection of neonatal seizures

This paper presents a novel approach to the extraction of video features for real-time detection of neonatal seizures. In particular, after identification of a proper Region Of Interest (ROI) within the video frame, the broadening factor and the maximum distance between consecutive pairs of zeros of a properly extracted average differential luminosity signal are shown to be relevant features for a diagnosis. The ROI is selected by defining an area around the point where the maximum amplitude of the optical flow vector of that video frame sequence is observed. The located point is then tracked by an algorithm based on template matching and optical flow. The proposed approach allows to differentiate pathological movements (e.g., clonic and myoclonic seizures) from random ones. © 2011 IEEE

Low-complexity image processing for real-time detection of neonatal clonic seizures

In this paper, we consider a novel low-complexity image processing-based approach to the detection of neonatal clonic seizures. Our approach is based on the extraction, from a video recording of a newborn, of an average luminosity signal representative of the body movements. Since clonic seizures are characterized by periodic movements of parts of the body (e.g., the limbs), by evaluating the periodicity of the extracted average luminosity signal it is possible to estimate the presence of a seizure. The periodicity is detected, through a hybrid autocorrelation-Yin estimation technique, on a per-window basis, where a window is constituted by a sequence of consecutive video frames. While we first consider single windows, we extend our approach to a scenario with interlaced windows. The performance of the proposed algorithm is investigated, in terms of sensitivity and specificity, considering video recordings of newborns affected by neonatal seizures. Our results show that the use of interlaced windows guarantees both sensitivity and specificity values above 90%

Low-complexity image processing for real-time detection of neonatal clonic seizures

In this paper, we consider a novel low-complexity realtime image-processing based approach to the detection of neonatal clonic seizures. Our approach is based on the extraction, from a video of a newborn, of an average luminance signal representative of the body movements. Since clonic seizures are characterized by periodic movements of parts of the body (e.g., the limbs), by evaluating the periodicity of the extracted average luminance signal it is possible to detect the presence of a clonic seizure. The periodicity is investigated, through a hybrid autocorrelation-Yin estimation technique, on a per-window basis, where a time window is defined as a sequence of consecutive video frames. While processing is first carried out on a single window basis, we extend our approach to interlaced windows. The performance of the proposed detection algorithm is investigated, in terms of sensitivity and specificity, through receiver operating characteristic curves, considering video recordings of newborns affected by neonatal seizures

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

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 IEEE

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

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

Extraction of video features for real-time detection of neonatal seizures

This paper presents a novel approach to the extraction of video features for real-time detection of neonatal seizures. In particular, after identification of a proper Region Of Interest (ROI) within the video frame, the broadening factor and the maximum distance between consecutive pairs of zeros of a properly extracted average differential luminosity signal are shown to be relevant features for a diagnosis. The ROI is selected by defining an area around the point where the maximum amplitude of the optical flow vector of that video frame sequence is observed. The located point is then tracked by an algorithm based on template matching and optical flow. The proposed approach allows to differentiate pathological movements (e.g., clonic and myoclonic seizures) from random ones

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

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

Real-time automated detection of clonic seizures in newborns

Objective: The aim of this study is to apply a real-time algorithm for clonic neonatal seizures detection, based on a low complexity image processing approach extracting the differential average luminance from videotaped body movements. Methods: 23 video-EEGs from 12 patients containing 78 electrographically confirmed neonatal seizures of clonic type were reviewed and all movements were divided into noise, random movements, clonic seizures or other seizure types. Six video-EEGs from 5 newborns without seizures were also reviewed. Videos were then separately analyzed using either single, double or triple windows (these latter with 50% overlap) each of a 10 s duration. Results: With a decision threshold set at 0.5, we obtained a sensitivity of 71% (corresponding specificity: 69%) with double-window processing for clonic seizures diagnosis. The discriminatory power, indicated by the Area Under the Curve (AUC), is higher with two interlaced windows (AUC = 0.796) than with single (AUC = 0.788) or triple-window (AUC = 0.728). Among subjects without neonatal seizures, our algorithm showed a specificity of 91% with double-window processing. Conclusions: Our algorithm reliably detects neonatal clonic seizures and differentiates them from either noise, random movements and other seizure types. Significance: It could represent a low-cost, low complexity, real-time automated screening tool for clonic neonatal seizures

Real-time automated detection of clonic seizures in newborns

Objective: The aim of this study is to apply a real-time algorithm for clonic neonatal seizures detection, based on a low complexity image processing approach extracting the differential average luminance from videotaped body movements. Methods: 23 video-EEGs from 12 patients containing 78 electrographically confirmed neonatal seizures of clonic type were reviewed and all movements were divided into noise, random movements, clonic seizures or other seizure types. Six video-EEGs from 5 newborns without seizures were also reviewed. Videos were then separately analyzed using either single, double or triple windows (these latter with 50% overlap) each of a 10. s duration. Results: With a decision threshold set at 0.5, we obtained a sensitivity of 71% (corresponding specificity: 69%) with double-window processing for clonic seizures diagnosis. The discriminatory power, indicated by the Area Under the Curve (AUC), is higher with two interlaced windows (AUC. = 0.796) than with single (AUC. = 0.788) or triple-window (AUC. = 0.728). Among subjects without neonatal seizures, our algorithm showed a specificity of 91% with double-window processing. Conclusions: Our algorithm reliably detects neonatal clonic seizures and differentiates them from either noise, random movements and other seizure types. Significance: It could represent a low-cost, low complexity, real-time automated screening tool for clonic neonatal seizures. © 2014 International Federation of Clinical Neurophysiology