Deep learning (DL) has been introduced in automatic heart-abnormality classification using ECG signals, while its application in practical medical procedures is limited. A systematic review is performed from perspectives of the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability to identify research trends, challenges, and opportunities for DL-based ECG arrhythmia classification. Specifically, 368 studies meeting the eligibility criteria are included. A total of 223 (61%) studies use MIT-BIH Arrhythmia Database to design DL models. A total of 138 (38%) studies considered removing noise or artifacts in ECG signals, and 102 (28%) studies performed data augmentation to extend the minority arrhythmia categories. Convolutional neural networks are the dominant models (58.7%, 216) used in the reviewed studies while growing studies have integrated multiple DL structures in recent years. A total of 319 (86.7%) and 38 (10.3%) studies explicitly mention their evaluation paradigms, i.e., intra- and inter-patient paradigms, respectively, where notable performance degradation is observed in the inter-patient paradigm. Compared to the overall accuracy, the average F1 score, sensitivity, and precision are significantly lower in the selected studies. To implement the DL-based ECG classification in real clinical scenarios, leveraging diverse ECG databases, designing advanced denoising and data augmentation techniques, integrating novel DL models, and deeper investigation in the inter-patient paradigm could be future research opportunities.

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Background Older persons are at high-risk of developing severe complications from influenza. This consensus statement was developed to provide guidance on appropriate influenza prevention strategies relevant to the Malaysian healthcare setting. Methods Under the initiative of the Malaysian Influenza Working Group (MIWG), a panel comprising 11 multi-speciality physicians was convened to develop a consensus statement. Using a modified Delphi process, the panellists reviewed published evidence on various influenza management interventions and synthesised 10 recommendations for the prevention of influenza among the aged population via group discussions and a blinded rating exercise. Results Overall, annual influenza vaccination is recommended for individuals aged ≥ 60 years, particularly those with specific medical conditions or residing in aged care facilities (ACFs). There is no preference for a particular vaccine type in this target population. Antiviral agents can be given for post-exposure chemoprophylaxis or when vaccine contraindication exists. Infection control measures should serve as adjuncts to prevent the spread of influenza, especially during Hajj. Conclusion This consensus statement presents 10 evidence-based recommendations that can be adopted by healthcare providers to prevent influenza among the aged population in Malaysia. It could also serve as a basis for health policy planning in other lower- and middle-income countries.

Plasmodium knowlesi is an emerging species for malaria in Malaysia, particularly in East Malaysia. This infection contributes to almost half of all malaria cases and deaths in Malaysia and poses a challenge in eradicating malaria. The aim of this study was to develop a predictive model for P. knowlesi susceptibility areas in Sabah, Malaysia, using geospatial data and artificial neural networks (ANNs). Weekly malaria cases from 2013 to 2014 were used to identify the malaria hotspot areas. The association of malaria cases with environmental factors (elevation, water bodies, and population density, and satellite images providing rainfall, land surface temperature, and normalized difference vegetation indices) were statistically determined. The significant environmental factors were used as input for the ANN analysis to predict malaria cases. Finally, the malaria susceptibility index and zones were mapped out. The results suggested integrating geospatial data and ANNs to predict malaria cases, with overall correlation coefficient of 0.70 and overall accuracy of 91.04%. From the malaria susceptibility index and zoning analyses, it was found that areas located along the Crocker Range of Sabah and the East part of Sabah were highly susceptible to P. knowlesi infections. Following this analysis, targetted entomological mapping and malaria control programs can be initiated.

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