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Mansor M.N.,University Malaysia Perlis | Yaacob S.,University Malaysia Perlis | Hariharan M.,University Malaysia Perlis | Basah S.N.,University Malaysia Perlis | And 7 more authors.
IFMBE Proceedings | Year: 2013

In this paper, tremble stage assessment is explained and reviewed for detecting facial changes of patient in a hospital in Neonatal Intensive Care Unit (NICU). The facial changes are most widely represented by eyes and mouth movements. The proposed system uses color images and it consists of three modules. The first module implements skin detection method to detect the face. Secondly, extracts the features of faces by processing the image and measuring certain dimensions face regions. Finally a knn and Fuzzy k- NN classifier used to classify the movements. From the experiments, it is found that the identification rate of reaches 93.30% and 70.25% respectively. © 2013 Springer-Verlag.


Mansor M.N.,University Malaysia Perlis | Yaacob S.,University Malaysia Perlis | Hariharan M.,University Malaysia Perlis | Basah S.N.,University Malaysia Perlis | And 7 more authors.
IFMBE Proceedings | Year: 2013

This paper presents the management of sedation in critically ill infants is a complex issue for Intensive Care Units (ICU) worldwide. Notable complications of sedation practices have been identified and efforts to modify these practices in ICUs have begun. While sedation-scoring tools have been introduced into clinical practice in intensive care few have been tested for validity and reliability. One tool which has reliability and validity established is the Sedation-Agitation Scale (SAS). This study is an extension of a previous study by Riker, Picard and Fraser (1999) to determine whether doctors and nurses rate infants similarly using the SAS in a natural ICU setting. It is essential to establish whether these different professionals provide consistent scores and have a mutual understanding of the SAS and its constituent levels based on LDA and SVM Cassifier. This will help ensure that clinical decisions relating to sedation-needs can be made appropriately and consistently. © 2013 Springer-Verlag.


Mansor M.N.,Intelligent Group | Hi-Fi Syam Ahmad Jamil S.,Politeknik Tuanku Syed Sirajuddin | Junoh A.K.,Intelligent Group | Rejab M.N.,Politeknik Tuanku Syed Sirajuddin | And 2 more authors.
2012 International Conference on Computer and Communication Engineering, ICCCE 2012 | Year: 2012

within this paper, pain detection is exposed and reviewed for detecting facial changes of patient in a hospital in Neonatal Intensive Care Unit (NICU). The system propesed three stage. The first stage implements Haar Cascade detection to detect the infant face. Secondly, PCA was employed for feature extraction. The third module extracts the PCA features of faces by measuring certain dimensions of pain and no pain regions with Support Vector Machine classifier. From 300 samples of face images, it is found that the identification rate of reaches 93.18%. © 2012 IEEE.

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