Determining Anxiety in Obsessive Compulsive Disorder through Behavioural Clustering and Variations in Repetition Intensity

Cameron, C, Khalil, I and Castle, D 2018, 'Determining Anxiety in Obsessive Compulsive Disorder through Behavioural Clustering and Variations in Repetition Intensity', Computer Methods and Programs in Biomedicine, vol. 160, pp. 65-74.


Document type: Journal Article
Collection: Journal Articles

Title Determining Anxiety in Obsessive Compulsive Disorder through Behavioural Clustering and Variations in Repetition Intensity
Author(s) Cameron, C
Khalil, I
Castle, D
Year 2018
Journal name Computer Methods and Programs in Biomedicine
Volume number 160
Start page 65
End page 74
Total pages 10
Publisher Elsevier
Abstract Background and objectives: Over the last decade, the application of computer vision techniques to the analysis of behavioural patterns has seen a considerable increase in research interest. One such interesting and recent application is the visual behavioural analysis of mental disorders. Despite the very recent surge in interest in this area, relatively little has been done thus far to assist individuals living with Obsessive Compulsive Disorder. The work proposed herein represents a proof of concept system designed to demonstrate the efficacy of such an approach, from the computational perspective. The specific focus of this work lies in demonstrating a mechanism for clustering different kinds of Obsessive Compulsive Disorder behaviours and then comparing new behaviours to existing behaviours to determine the approximate level of anxiety represented by a compulsive behaviour. Methods: The proposed system uses Temporal Motion Heat Maps, SURF descriptors, a visual bag of words model and SVM-based classification to categorise representations of various behaviours commonly seen in OCD. Moreover, we apply a set of statistical measures to the images in a given category in order to derive an approximate anxiety level for a given compulsive behaviour. This proof of concept is an essential step in the direction of integrating computational approaches into the treatment of psychiatric conditions such as Obsessive Compulsive Disorder, for more effective recovery. Results: Results gleaned from experimental simulations indicate that the proposed system is capable of correctly classifying different types of simulated Obsessive Compulsive Disorder behaviour classes 75% of the time, with the misclassifications almost exclusively occurring when two behavioural clusters appear highly similar. Based on this information the proposed system is then able to assign an approximate behavioural anxiety level to the compulsive behaviours that meets the approval of a mental health profession
Subject Biomedical Engineering not elsewhere classified
Artificial Intelligence and Image Processing not elsewhere classified
Electrical and Electronic Engineering not elsewhere classified
Keyword(s) Anxiety analysis
Behaviour classification
Compulsive behaviour
Obsessive compulsive disorder
SURF
DOI - identifier 10.1016/j.cmpb.2018.03.019
Copyright notice © 2018 Elsevier B.V. All rights reserved.
ISSN 0169-2607
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 8 Abstract Views  -  Detailed Statistics
Created: Tue, 26 Mar 2019, 09:36:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us