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Asphyxia screening kit

Zabidi A, Khuan LY, Mansor W

Annu Int Conf IEEE Eng Med Biol Soc, 2012;2012:1298-301.
PMID: 23366136 DOI: 10.1109/EMBC.2012.6346175

Infant asphyxia is a condition due to insufficient oxygen intake suffered by newborn babies. A 4 to 9 million occurrences of infant asphyxia are reported each year by WHO. Early diagnosis of asphyxia is important to avoid complications such as damage to the brain, organ and tissue that could lead to fatality. This is possible with the automation of screening of infant asphyxia. Here, a non-invasive Asphyxia Screening Kit is developed. It is a Graphical User Interface that automatically detects asphyxia in infants from early birth to 6 months from their cries and displays the outcome of analysis. It is built with Matlab GUI underlied with signal processing algorithms, capable of achieving a classification accuracy of 96.03%. Successful implementation of ASK will assist to screen infant asphyxia for reference to clinicians for early diagnosis. In addition, ASK also provides an interface to enter patient information and images to be integrated with existing Hospital Information Management System.
Binary particle swarm optimization for feature selection in detection of infants with hypothyroidism

Zabidi A, Khuan LY, Mansor W, Yassin IM, Sahak R

Annu Int Conf IEEE Eng Med Biol Soc, 2011;2011:2772-5.
PMID: 22254916 DOI: 10.1109/IEMBS.2011.6090759

Hypothyroidism in infants is caused by the insufficient production of hormones by the thyroid gland. Due to stress in the chest cavity as a result of the enlarged liver, their cry signals are unique and can be distinguished from the healthy infant cries. This study investigates the effect of feature selection with Binary Particle Swarm Optimization on the performance of MultiLayer Perceptron classifier in discriminating between the healthy infants and infants with hypothyroidism from their cry signals. The feature extraction process was performed on the Mel Frequency Cepstral coefficients. Performance of the MLP classifier was examined by varying the number of coefficients. It was found that the BPSO enhances the classification accuracy while reducing the computation load of the MLP classifier. The highest classification accuracy of 99.65% was achieved for the MLP classifier, with 36 filter banks, 5 hidden nodes and 11 BPS optimised MFC coefficients.
Optimization of MFCC parameters using Particle Swarm Optimization for diagnosis of infant hypothyroidism using Multi- Layer Perceptron

Zabidi A, Lee YK, Mansor W, Yassin IM, Sahak R

Annu Int Conf IEEE Eng Med Biol Soc, 2010;2010:1417-20.
PMID: 21096346 DOI: 10.1109/IEMBS.2010.5626712

This paper presents a new application of the Particle Swarm Optimization (PSO) algorithm to optimize Mel Frequency Cepstrum Coefficients (MFCC) parameters, in order to extract an optimal feature set for diagnosis of hypothyroidism in infants using Multi-Layer Perceptrons (MLP) neural network. MFCC features is influenced by the number of filter banks (f(b)) and the number of coefficients (n(c)) used. These parameters are critical in representation of the features as they affect the resolution and dimensionality of the features. In this paper, the PSO algorithm was used to optimize the values of f(b) and n(c). The MFCC features based on the PSO optimization were extracted from healthy and unhealthy infant cry signals and used to train MLP in the classification of hypothyroid infant cries. The results indicate that the PSO algorithm could determine the optimum combination of f(b) and n(c) that produce the best classification accuracy of the MLP.
Performance of Combined Support Vector Machine and Principal Component Analysis in recognizing infant cry with asphyxia

Sahak R, Mansor W, Lee YK, Yassin AM, Zabidi A

Annu Int Conf IEEE Eng Med Biol Soc, 2010;2010:6292-5.
PMID: 21097359 DOI: 10.1109/IEMBS.2010.5628084

Combined Support Vector Machine (SVM) and Principal Component Analysis (PCA) was used to recognize the infant cries with asphyxia. SVM classifier based on features selected by the PCA was trained to differentiate between pathological and healthy cries. The PCA was applied to reduce dimensionality of the vectors that serve as inputs to the SVM. The performance of the SVM utilizing linear and RBF kernel was examined. Experimental results showed that SVM with RBF kernel yields good performance. The classification accuracy in classifying infant cry with asphyxia using the SVM-PCA is 95.86%.
Simulation of the generation and processing of Doppler ultrasound fetal heart signals to obtain directional motion information

Mansor W, Crowe JA, Woolfson M, Hayes-Gill BR, Blanchfield P, Bister M

Conf Proc IEEE Eng Med Biol Soc, 2007 10 20;2006:1383-6.
PMID: 17945640

In fetal heart monitoring using Doppler ultrasound signals the cardiac information is commonly extracted from non-directional signals. As a consequence often some of the cardiac events cannot be observed clearly which may lead to the incorrect detection of the valve and wall motions. Here, directional signals were simulated to investigate their enhancement of cardiac events, and hence provide clearer information regarding the cardiac activities. First, fetal Doppler ultrasound signals were simulated with signals encoding forward and reverse motion then obtained using a pilot frequency. The simulation results demonstrate that the model has the ability to produce realistic Doppler ultrasound signals and a pilot frequency can be used in the mixing process to produce directional signals that allow the simulated cardiac events to be distinguished clearly and correctly.
Fulltext Mirror Therapy Rehabilitation in Stroke: A Scoping Review of Upper Limb Recovery and Brain Activities

Jaafar N, Che Daud AZ, Ahmad Roslan NF, Mansor W

Rehabil Res Pract, 2021;2021:9487319.
PMID: 35003808 DOI: 10.1155/2021/9487319

Background: Mirror therapy (MT) has been used as a treatment for various neurological disorders. Recent application of electroencephalogram (EEG) to the MT study allows researchers to gain insight into the changes in brain activity during the therapy.
Objective: This scoping review is aimed at mapping existing evidence and identifying knowledge gaps about the effects of MT on upper limb recovery and its application for individuals with chronic stroke.
Methods and Materials: A scoping review through a systematic literature search was conducted using PubMed, CINAHL, PsycINFO, and Scopus databases. Twenty articles published between 2010 and 2020 met the inclusion criteria. The efficacy of MT on upper limb recovery and brain activity during MT were discussed according to the International Classification of Functioning, Disability and Health (ICF).
Results: A majority of the studies indicated positive effects of MT on upper limb recovery from the body structure/functional domain. All studies used EEG to indicate brain activation during MT.
Conclusion: MT is a promising intervention for improving upper limb function for individuals with chronic stroke. This review also highlights the need to incorporate EEG into the MT study to capture brain activity and understand the mechanism underlying the therapy.
Fulltext Identification of Chikungunya virus strains circulating in Kelantan, Malaysia in 2009

Apandi Y, Lau SK, Izmawati N, Amal NM, Faudzi Y, Mansor W, et al.

Southeast Asian J. Trop. Med. Public Health, 2010 Nov;41(6):1374-80.
PMID: 21329313

Malaysia experienced its first outbreak of chikungunya virus (CHIKV) infection in late 1998 in Klang District in Selangor; six years later the virus re-emerged in the state of Perak. All the CHIKV isolates in 1988 and 2006 shared high sequence similarities and belonged to the Asian genotype. In 2007 and 2008 CHIKV infection again reemerged but the genotype was the Central/East African genotype. This strain was found to be similar to the strains causing outbreaks in the India Ocean. In 2009, the strains circulating in Malaysia, including the state of Kelantan, based on the partial E1 gene, also belong to the Central/East African genotype.