Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signal.

Automated epileptic seizure detection from ectroencephalogram (EEG) signals has attracted significant attention in the recent health informatics field. The serious brain condition known as epilepsy, which is characterized by recurrent seizures, is typically described as a sudden change in behavior caused by a momentary shift in the excessive electrical discharges in a group of brain cells, and EEG signal is primarily used in most cases to identify seizure to revitalize the close loop brain. The development of various deep learning (DL) algorithms for epileptic seizure diagnosis has been driven by the EEG's non-invasiveness and capacity to provide repetitive patterns of seizure-related electrophysiological information. Existing DL models, especially in clinical contexts where irregular and unordered structures of physiological recordings make it difficult to think of them as a matrix; this has been a key disadvantage to producing a consistent and appropriate diagnosis outcome due to EEG's low amplitude and nonstationary nature. Graph neural networks have drawn significant improvement by exploiting implicit information that is present in a brain anatomical system, whereas inter-acting nodes are connected by edges whose weights can be determined by either temporal associations or anatomical connections. Considering all these aspects, a novel hybrid framework is proposed for epileptic seizure detection by combined with a sequential graph convolutional network (SGCN) and deep recurrent neural network (DeepRNN). Here, DepRNN is developed by fusing a gated recurrent unit (GRU) with a traditional RNN; its key benefit is that it solves the vanishing gradient problem and achieve this hybrid framework greater sophistication. The line length feature, auto-covariance, auto-correlation, and periodogram are applied as a feature from the raw EEG signal and then grouped the resulting matrix into time-frequency domain as inputs for the SGCN to use for seizure classification. This model extracts both spatial and temporal information, resulting in improved accuracy, precision, and recall for seizure detection. Extensive experiments conducted on the CHB-MIT and TUH datasets showed that the SGCN-DeepRNN model outperforms other deep learning models for seizure detection, achieving an accuracy of 99.007%, with high sensitivity and specificity.

Health problems among Forcibly Displaced Myanmar Nationals (FDMNs) admitted to the Medicine ward of Cox's Bazar Medical College Hospital.

Background: Forcibly Displaced Myanmar Nationals (FDMNs) or Rohingya refugees are one of the vulnerable groups suffering from different kinds of health problems but have been less reported yet. Therefore, the study was designed to delineate the health problems among FDMNs admitted to Cox's Bazar Medical College Hospital. Methods: This hospital-based cross-sectional study was conducted at the Medicine ward, Cox's Bazar Medical College Hospital, for a six-month period following approval. Rohingya refugees who were admitted during the study period were approached for inclusion. Informed written consent was ensured prior to participation. A structured questionnaire was used during data collection. Collected information was recorded in case record form. A total of 290 subjects were interviewed. Analysis was performed using the statistical package for social science (SPSS) version 20. Results: The mean age of the participants was 48.76 ± 18.67 years (range: 16-91), with a clear male predominance (60.7%). Family size ranged 6-8. All of the participants reported at least one of the illnesses. Of all, 29.66% patients had disease of the respiratory system, and 26.9% had disease of the gastrointestinal and hepatobiliary system. Accidental injury or injury due to electrocution or thin falls or snake bites was present in 10.4% of the cases. Among the single most common diseases, COPD (20%) was the most frequently observed, and the rest of them were chronic liver disease (13.1%), pulmonary TB (5.5%), ischemic stroke (5.5%), CAP (4.1%), acute coronary syndrome (3.4%), thalassaemia (3.4%) and hepatocellular carcinoma (3.4%). Among the top 6 diagnosed diseases, PTB was more common in elderly individuals (p = 0.29). The disease pattern was similar across the sexes among the refugees except community acquisition pneumonia (CAP), which was commonly observed among males (p = .004). Considering different age groups, genitourinary problems were more common in males aged >60 years, and rheumatology and musculoskeletal problems were equally affected in females aged between 40 and 60 years. Conclusion: COPD, CLD and CAP were the most prevalent diseases in FDMN patients who attended the Medicine ward of Cox's Bazar Medical College Hospital. Further exploration is warranted before any policy making and comprehensive plan.

Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development.

BACKGROUND: Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction. OBJECTIVE: Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes. METHODS: We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods. RESULTS: Our work revealed that several clinical parameters that are measurable in blood samples are factors that can discriminate between healthy people and COVID-19-positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores >90% for disease severity prediction. CONCLUSIONS: We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment.