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Fulltext Early Detection of Silent Hypoxia in Covid-19 Pneumonia Using Smartphone Pulse Oximetry

Teo J

J Med Syst, 2020 Jun 19;44(8):134.
PMID: 32562006 DOI: 10.1007/s10916-020-01587-6
Fulltext Commentary: Strategies to Address COVID-19 Vaccine Hesitancy and Mitigate Health Disparities in Minority Populations

Teo J

Front Public Health, 2021;9:723583.
PMID: 34395375 DOI: 10.3389/fpubh.2021.723583
Automatic generation of controllers for embodied legged organisms: a Pareto evolutionary multi-objective approach

Teo J, Abbass HA

Evol Comput, 2004;12(3):355-94.
PMID: 15355605

In this paper, we investigate the use of a self-adaptive Pareto evolutionary multi-objective optimization (EMO) approach for evolving the controllers of virtual embodied organisms. The objective of this paper is to demonstrate the trade-off between quality of solutions and computational cost. We show empirically that evolving controllers using the proposed algorithm incurs significantly less computational cost when compared to a self-adaptive weighted sum EMO algorithm, a self-adaptive single-objective evolutionary algorithm (EA) and a hand-tuned Pareto EMO algorithm. The main contribution of the self-adaptive Pareto EMO approach is its ability to produce sufficiently good controllers with different locomotion capabilities in a single run, thereby reducing the evolutionary computational cost and allowing the designer to explore the space of good solutions simultaneously. Our results also show that self-adaptation was found to be highly beneficial in reducing redundancy when compared against the other algorithms. Moreover, it was also shown that genetic diversity was being maintained naturally by virtue of the system's inherent multi-objectivity.
Term viable abdominal pregnancy: a case report and review

Vela V, Teo J

Aust N Z J Obstet Gynaecol, 2001 Aug;41(3):344-5.
PMID: 11592558 DOI: 10.1111/j.1479-828x.2001.tb01245.x
Single classifier vs. ensemble machine learning approaches for mental health prediction

Chung J, Teo J

Brain Inform, 2023 Jan 03;10(1):1.
PMID: 36595134 DOI: 10.1186/s40708-022-00180-6

Early prediction of mental health issues among individuals is paramount for early diagnosis and treatment by mental health professionals. One of the promising approaches to achieving fully automated computer-based approaches for predicting mental health problems is via machine learning. As such, this study aims to empirically evaluate several popular machine learning algorithms in classifying and predicting mental health problems based on a given data set, both from a single classifier approach as well as an ensemble machine learning approach. The data set contains responses to a survey questionnaire that was conducted by Open Sourcing Mental Illness (OSMI). Machine learning algorithms investigated in this study include Logistic Regression, Gradient Boosting, Neural Networks, K-Nearest Neighbours, and Support Vector Machine, as well as an ensemble approach using these algorithms. Comparisons were also made against more recent machine learning approaches, namely Extreme Gradient Boosting and Deep Neural Networks. Overall, Gradient Boosting achieved the highest overall accuracy of 88.80% followed by Neural Networks with 88.00%. This was followed by Extreme Gradient Boosting and Deep Neural Networks at 87.20% and 86.40%, respectively. The ensemble classifier achieved 85.60% while the remaining classifiers achieved between 82.40 and 84.00%. The findings indicate that Gradient Boosting provided the highest classification accuracy for this particular mental health bi-classification prediction task. In general, it was also demonstrated that the prediction results produced by all of the machine learning approaches studied here were able to achieve more than 80% accuracy, thereby indicating a highly promising approach for mental health professionals toward automated clinical diagnosis.
Fulltext Aesthetic preference recognition of 3D shapes using EEG

Chew LH, Teo J, Mountstephens J

Cogn Neurodyn, 2016 Apr;10(2):165-73.
PMID: 27066153 DOI: 10.1007/s11571-015-9363-z

Recognition and identification of aesthetic preference is indispensable in industrial design. Humans tend to pursue products with aesthetic values and make buying decisions based on their aesthetic preferences. The existence of neuromarketing is to understand consumer responses toward marketing stimuli by using imaging techniques and recognition of physiological parameters. Numerous studies have been done to understand the relationship between human, art and aesthetics. In this paper, we present a novel preference-based measurement of user aesthetics using electroencephalogram (EEG) signals for virtual 3D shapes with motion. The 3D shapes are designed to appear like bracelets, which is generated by using the Gielis superformula. EEG signals were collected by using a medical grade device, the B-Alert X10 from advance brain monitoring, with a sampling frequency of 256 Hz and resolution of 16 bits. The signals obtained when viewing 3D bracelet shapes were decomposed into alpha, beta, theta, gamma and delta rhythm by using time-frequency analysis, then classified into two classes, namely like and dislike by using support vector machines and K-nearest neighbors (KNN) classifiers respectively. Classification accuracy of up to 80 % was obtained by using KNN with the alpha, theta and delta rhythms as the features extracted from frontal channels, Fz, F3 and F4 to classify two classes, like and dislike.
Fulltext Eye-Tracking Feature Extraction for Biometric Machine Learning

Lim JZ, Mountstephens J, Teo J

Front Neurorobot, 2021;15:796895.
PMID: 35177973 DOI: 10.3389/fnbot.2021.796895

CONTEXT: Eye tracking is a technology to measure and determine the eye movements and eye positions of an individual. The eye data can be collected and recorded using an eye tracker. Eye-tracking data offer unprecedented insights into human actions and environments, digitizing how people communicate with computers, and providing novel opportunities to conduct passive biometric-based classification such as emotion prediction. The objective of this article is to review what specific machine learning features can be obtained from eye-tracking data for the classification task.
METHODS: We performed a systematic literature review (SLR) covering the eye-tracking studies in classification published from 2016 to the present. In the search process, we used four independent electronic databases which were the IEEE Xplore, the ACM Digital Library, and the ScienceDirect repositories as well as the Google Scholar. The selection process was performed by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) search strategy. We followed the processes indicated in the PRISMA to choose the appropriate relevant articles.
RESULTS: Out of the initial 420 articles that were returned from our initial search query, 37 articles were finally identified and used in the qualitative synthesis, which were deemed to be directly relevant to our research question based on our methodology.
CONCLUSION: The features that could be extracted from eye-tracking data included pupil size, saccade, fixations, velocity, blink, pupil position, electrooculogram (EOG), and gaze point. Fixation was the most commonly used feature among the studies found.
Fulltext Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges

Lim JZ, Mountstephens J, Teo J

Sensors (Basel), 2020 Apr 22;20(8).
PMID: 32331327 DOI: 10.3390/s20082384

The ability to detect users' emotions for the purpose of emotion engineering is currently one of the main endeavors of machine learning in affective computing. Among the more common approaches to emotion detection are methods that rely on electroencephalography (EEG), facial image processing and speech inflections. Although eye-tracking is fast in becoming one of the most commonly used sensor modalities in affective computing, it is still a relatively new approach for emotion detection, especially when it is used exclusively. In this survey paper, we present a review on emotion recognition using eye-tracking technology, including a brief introductory background on emotion modeling, eye-tracking devices and approaches, emotion stimulation methods, the emotional-relevant features extractable from eye-tracking data, and most importantly, a categorical summary and taxonomy of the current literature which relates to emotion recognition using eye-tracking. This review concludes with a discussion on the current open research problems and prospective future research directions that will be beneficial for expanding the body of knowledge in emotion detection using eye-tracking as the primary sensor modality.
Fulltext EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities

Suhaimi NS, Mountstephens J, Teo J

Comput Intell Neurosci, 2020;2020:8875426.
PMID: 33014031 DOI: 10.1155/2020/8875426

Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing interest of the research community towards establishing some meaningful "emotional" interactions between humans and computers, the need for reliable and deployable solutions for the identification of human emotional states is required. Recent developments in using electroencephalography (EEG) for emotion recognition have garnered strong interest from the research community as the latest developments in consumer-grade wearable EEG solutions can provide a cheap, portable, and simple solution for identifying emotions. Since the last comprehensive review was conducted back from the years 2009 to 2016, this paper will update on the current progress of emotion recognition using EEG signals from 2016 to 2019. The focus on this state-of-the-art review focuses on the elements of emotion stimuli type and presentation approach, study size, EEG hardware, machine learning classifiers, and classification approach. From this state-of-the-art review, we suggest several future research opportunities including proposing a different approach in presenting the stimuli in the form of virtual reality (VR). To this end, an additional section devoted specifically to reviewing only VR studies within this research domain is presented as the motivation for this proposed new approach using VR as the stimuli presentation device. This review paper is intended to be useful for the research community working on emotion recognition using EEG signals as well as for those who are venturing into this field of research.
Lung function reference values in Singaporean children aged 6-18 years

Connett GJ, Quak SH, Wong ML, Teo J, Lee BW

Thorax, 1994 Sep;49(9):901-5.
PMID: 7940431

A study was undertaken to produce reference values of lung function in Chinese children and a means of calculating adjusted standard deviation scores of lung function for Malay and Indian ethnic groups.
Severe acute respiratory syndrome-Coronavirus-2 infection: A review of the clinical-pathological correlations of Coronavirus disease-19 in children

Teo JTR, Abidin NH, Cheah FC

Malays J Pathol, 2020 12;42(3):349-361.
PMID: 33361715

The coronavirus disease-19 (COVID-19) has become a global pandemic of acute respiratory disease in just less than a year by the middle of 2020. This disease caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has resulted in significant mortality especially among the older age population and those with health co-morbidities. In contrast, children are relatively spared of this potentially ravaging disease that culminates in the acute respiratory distress syndrome, multi-organ failure and death. SARS-CoV-2 infection induces exuberant release of pro-inflammatory mediators, causing a "cytokine storm" and hypercoagulable states that underlie these complications. The SARS-CoV-2 infection median incubation is 5.1 days, with most developing symptoms by 11.5 days. It is highly infectious, spreading via the horizontal mode of transmission, but there is yet very limited evidence of vertical transmission to the newborn infant occurring either transplacentally or through breastfeeding. This said, various immune factors during childhood may modulate the expression of COVID-19, with the multisystem inflammatory syndrome in children (MIS-C) at the severe end of the disease spectrum. This article gives an overview of the SARS-CoV-2 infection, clinical presentation and laboratory tests of COVID-19 and correlating with the current understanding of the pathological basis of this disease in the paediatric population.
Fulltext A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things

Khan ZA, Naz S, Khan R, Teo J, Ghani A, Almaiah MA

Comput Intell Neurosci, 2022;2022:5112375.
PMID: 35449734 DOI: 10.1155/2022/5112375

Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of nonneighbor is not compared at all. These algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches.
Fulltext An Industrial IoT-Based Blockchain-Enabled Secure Searchable Encryption Approach for Healthcare Systems Using Neural Network

Ali A, Almaiah MA, Hajjej F, Pasha MF, Fang OH, Khan R, et al.

Sensors (Basel), 2022 Jan 12;22(2).
PMID: 35062530 DOI: 10.3390/s22020572

The IoT refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges such as security, trustworthiness, reliability, confidentiality, and so on. To address these issues, we have proposed a novel group theory (GT)-based binary spring search (BSS) algorithm which consists of a hybrid deep neural network approach. The proposed approach effectively detects the intrusion within the IoT network. Initially, the privacy-preserving technology was implemented using a blockchain-based methodology. Security of patient health records (PHR) is the most critical aspect of cryptography over the Internet due to its value and importance, preferably in the Internet of Medical Things (IoMT). Search keywords access mechanism is one of the typical approaches used to access PHR from a database, but it is susceptible to various security vulnerabilities. Although blockchain-enabled healthcare systems provide security, it may lead to some loopholes in the existing state of the art. In literature, blockchain-enabled frameworks have been presented to resolve those issues. However, these methods have primarily focused on data storage and blockchain is used as a database. In this paper, blockchain as a distributed database is proposed with a homomorphic encryption technique to ensure a secure search and keywords-based access to the database. Additionally, the proposed approach provides a secure key revocation mechanism and updates various policies accordingly. As a result, a secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data. Hence, our proposed approach provides more security, efficiency, and transparency with cost-effectiveness. We performed our simulations based on the blockchain-based tool Hyperledger Fabric and OrigionLab for analysis and evaluation. We compared our proposed results with the benchmark models, respectively. Our comparative analysis justifies that our proposed framework provides better security and searchable mechanism for the healthcare system.
Experience of a rapid access falls and syncope service at a teaching hospital in Kuala Lumpur

Gan SY, Saedon NI, Sukanya S, Fairuz NHA, Sakinah SMN, Fatin NIAH, et al.

Med J Malaysia, 2017 08;72(4):203-208.
PMID: 28889130 MyJurnal

No abstract available.
Diagnostic accuracy of the Xpert® MTB/RIF cycle threshold level to predict smear positivity: a meta-analysis

Lange B, Khan P, Kalmambetova G, Al-Darraji HA, Alland D, Antonenka U, et al.

Int J Tuberc Lung Dis, 2017 05 01;21(5):493-502.
PMID: 28399963 DOI: 10.5588/ijtld.16.0702

SETTING: Xpert® MTB/RIF is the most widely used molecular assay for rapid diagnosis of tuberculosis (TB). The number of polymerase chain reaction cycles after which detectable product is generated (cycle threshold value, CT) correlates with the bacillary burden.OBJECTIVE To investigate the association between Xpert CT values and smear status through a systematic review and individual-level data meta-analysis.
DESIGN: Studies on the association between CT values and smear status were included in a descriptive systematic review. Authors of studies including smear, culture and Xpert results were asked for individual-level data, and receiver operating characteristic curves were calculated.
RESULTS: Of 918 citations, 10 were included in the descriptive systematic review. Fifteen data sets from studies potentially relevant for individual-level data meta-analysis provided individual-level data (7511 samples from 4447 patients); 1212 patients had positive Xpert results for at least one respiratory sample (1859 samples overall). ROC analysis revealed an area under the curve (AUC) of 0.85 (95%CI 0.82-0.87). Cut-off CT values of 27.7 and 31.8 yielded sensitivities of 85% (95%CI 83-87) and 95% (95%CI 94-96) and specificities of 67% (95%CI 66-77) and 35% (95%CI 30-41) for smear-positive samples.
CONCLUSION: Xpert CT values and smear status were strongly associated. However, diagnostic accuracy at set cut-off CT values of 27.7 or 31.8 would not replace smear microscopy. How CT values compare with smear microscopy in predicting infectiousness remains to be seen.
Impact of COVID-19 pandemic on carbapenem-resistant Enterobacterales incidence in the South-East Asia region: an observational study

Linn KZ, Sutjipto S, Ng OT, Teo J, Cherng BPZ, Tan TY, et al.

Antimicrob Steward Healthc Epidemiol, 2023;3(1):e208.
PMID: 38156208 DOI: 10.1017/ash.2023.477

The COVID-19 pandemic led to an initial increase in the incidence of carbapenem-resistant Enterobacterales (CRE) from clinical cultures in South-East Asia hospitals, which was unsustained as the pandemic progressed. Conversely, there was a decrease in CRE incidence from surveillance cultures and overall combined incidence. Further studies are needed for future pandemic preparedness.
ACORN (A Clinically-Oriented Antimicrobial Resistance Surveillance Network) II: protocol for case based antimicrobial resistance surveillance

Mo Y, Ding Y, Cao Y, Hopkins J, Ashley EA, Waithira N, et al.

Wellcome Open Res, 2023;8:179.
PMID: 37854055 DOI: 10.12688/wellcomeopenres.19210.2

Background: Antimicrobial resistance surveillance is essential for empiric antibiotic prescribing, infection prevention and control policies and to drive novel antibiotic discovery. However, most existing surveillance systems are isolate-based without supporting patient-based clinical data, and not widely implemented especially in low- and middle-income countries (LMICs). Methods: A Clinically-Oriented Antimicrobial Resistance Surveillance Network (ACORN) II is a large-scale multicentre protocol which builds on the WHO Global Antimicrobial Resistance and Use Surveillance System to estimate syndromic and pathogen outcomes along with associated health economic costs. ACORN-healthcare associated infection (ACORN-HAI) is an extension study which focuses on healthcare-associated bloodstream infections and ventilator-associated pneumonia. Our main aim is to implement an efficient clinically-oriented antimicrobial resistance surveillance system, which can be incorporated as part of routine workflow in hospitals in LMICs. These surveillance systems include hospitalised patients of any age with clinically compatible acute community-acquired or healthcare-associated bacterial infection syndromes, and who were prescribed parenteral antibiotics. Diagnostic stewardship activities will be implemented to optimise microbiology culture specimen collection practices. Basic patient characteristics, clinician diagnosis, empiric treatment, infection severity and risk factors for HAI are recorded on enrolment and during 28-day follow-up. An R Shiny application can be used offline and online for merging clinical and microbiology data, and generating collated reports to inform local antibiotic stewardship and infection control policies. Discussion: ACORN II is a comprehensive antimicrobial resistance surveillance activity which advocates pragmatic implementation and prioritises improving local diagnostic and antibiotic prescribing practices through patient-centred data collection. These data can be rapidly communicated to local physicians and infection prevention and control teams. Relative ease of data collection promotes sustainability and maximises participation and scalability. With ACORN-HAI as an example, ACORN II has the capacity to accommodate extensions to investigate further specific questions of interest.

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