Intelligent Telehealth System To Support Epilepsy Diagnosis

EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population

BACKGROUND

Epilepsy is one of the most common neurological conditions globally, and the fourth most common in the United States. Recurrent non-provoked seizures characterize it and have huge impacts on the quality of life and financial impacts for affected individuals. A rapid and accurate diagnosis is essential in order to instigate and monitor optimal treatments. There is also a compelling need for the accurate interpretation of epilepsy due to the current scarcity in neurologist diagnosticians and a global inequity in access and outcomes. Furthermore, the existing clinical and traditional machine learning diagnostic methods exhibit limitations, warranting the need to create an automated system using deep learning model for epilepsy detection and monitoring using a huge database.

METHOD

The EEG signals from 35 channels were used to train the deep learning-based transformer model named (EpilepsyNet). For each training iteration, 1-min-long data were randomly sampled from each participant. Thereafter, each 5-s epoch was mapped to a matrix using the Pearson Correlation Coefficient (PCC), such that the bottom part of the triangle was discarded and only the upper triangle of the matrix was vectorized as input data. PCC is a reliable method used to measure the statistical relationship between two variables. Based on the 5 s of data, single embedding was performed thereafter to generate a 1-dimensional array of signals. In the final stage, a positional encoding with learnable parameters was added to each correlation coefficient's embedding before being fed to the developed EpilepsyNet as input data to epilepsy EEG signals. The ten-fold cross-validation technique was used to generate the model.

RESULTS

Our transformer-based model (EpilepsyNet) yielded high classification accuracy, sensitivity, specificity and positive predictive values of 85%, 82%, 87%, and 82%, respectively.

CONCLUSION

The proposed method is both accurate and robust since ten-fold cross-validation was employed to evaluate the performance of the model. Compared to the deep models used in existing studies for epilepsy diagnosis, our proposed method is simple and less computationally intensive. This is the earliest study to have uniquely employed the positional encoding with learnable parameters to each correlation coefficient's embedding together with the deep transformer model, using a huge database of 121 participants for epilepsy detection. With the training and validation of the model using a larger dataset, the same study approach can be extended for the detection of other neurological conditions, with a transformative impact on neurological diagnostics worldwide.

Applications of Medical Digital Technologies for Noncommunicable Diseases for Follow-Up during the COVID-19 Pandemic

BACKGROUND

Noncommunicable chronic diseases (NCDs) are multifaceted, and the health implications of the COVID-19 pandemic are far-reaching, especially for NCDs. Physical distancing and quarantine can lead to the poor management of NCDs because the visual tracking of them has been replaced with medical digital technology, that is, smartphone apps. This study aimed to explore medical digital technology applications for NCDs for follow-up during the COVID-19 pandemic.

METHODS

The participants in this study were 400 adult patients with NCDs; they were selected by systematic random sampling. A descriptive cross-sectional design was used. The study was conducted in the outpatient department of Yanbu General Hospital and primary-care health centers in Yanbu Al-Baher, Al-Madinah Al-Munawwarah, in the Kingdom of Saudi Arabia. The tools used in this study were a structured questionnaire to collect the sociodemographic characteristics of the patients and their health history, an NCD questionnaire to assess follow-up of the patients during the COVID-19 pandemic, and a medical digital technologies questionnaire to explore the medical digital technology applications.

RESULTS

The mean age of the patients was 47.32 ± 14.362 years, and 62.8% of them were female and 372 were male. Of the patients, 69.2% and 57.5% had been diagnosed with diabetes mellitus and hypertension, respectively; 52.4% were followed up monthly, and 29.75% used medical digital technology applications such as Tabeby Online to monitor their health. Furthermore, 71.75% and 75.5% of the patients used the Sehhaty and Tawakkalna medical digital applications, respectively. Overall, 38.7% of the patients were satisfied with using medical digital technology applications used for follow-up during the COVID-19 pandemic.

CONCLUSIONS

The study concluded that the services that use networks, smartphones, and medical digital technology applications on the Saudi Ministry of Health website and mobile applications to improve the quality of the health-care system, and that provide health services for noncommunicable or communicable diseases, are not effective. This is because the patients lack awareness of these services, with most of the chronic patients being elderly with lower levels of education and computer literacy.

Exploring and predicting mortality among patients with end-stage liver disease without cancer: a machine learning approach

OBJECTIVE

End-stage liver disease is a global public health problem with a high mortality rate. Early identification of people at risk of poor prognosis is fundamental for decision-making in clinical settings. This study created a machine learning prediction system that provides several related models with visualized graphs, including decision trees, ensemble learning and clustering, to predict mortality in patients with end-stage liver disease.

METHODS

A retrospective cohort study was conducted: the training data were from patients enrolled from January 2009 to December 2010 and followed up to December 2014; validation data were from patients enrolled from January 2015 to December 2016 and followed up to January 2019. Hospitalized patients with noncancer-related chronic liver disease were identified from the hospital's electrical medical records.

RESULTS

In traditional multivariable logistic regression and Cox proportional hazard model, prothrombin time of international normalized ratio, which was significant with P value = 0.002, odds ratio = 2.790 and hazard ratio 1.363. Besides, blood urea nitrogen and C-reactive protein were also significant, with P value <0.001 and 0.026. The area under the curve was 0.771 in the receiver operating characteristic curve. In machine learning, blood urea nitrogen and age were regarded as the primary factors for predicting mortality. Creatinine, prothrombin time of international normalized ratio and bilirubin were also significant mortality predictors. The area under the curve of the random forest and AdaBoost was 0.838 and 0.792.

CONCLUSION

The machine learning techniques provided a comprehensive assessment of patient conditions; it could help physicians make an accurate diagnosis of chronic liver disease and improve healthcare management.