SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification

Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease. This task is challenging due to factors such as low signal-to-noise ratios, signal artefacts, high variance in seizure semiology among epileptic patients, and limited availability of clinical data. To overcome these challenges, in this paper, we present SeizureNet, a deep learning framework which learns multi-spectral feature embeddings using an ensemble architecture for cross-patient seizure type classification. We used the recently released TUH EEG Seizure Corpus (V1.4.0 and V1.5.2) to evaluate the performance of SeizureNet. Experiments show that SeizureNet can reach a weighted F1 score of up to 0.94 for seizure-wise cross validation and 0.59 for patient-wise cross validation for scalp EEG based multi-class seizure type classification. We also show that the high-level feature embeddings learnt by SeizureNet considerably improve the accuracy of smaller networks through knowledge distillation for applications with low-memory constraints

The Impact Of A Structured Counseling Technique By Pharmacist On Knowledge, Satisfaction, Quality Of Life, Adherence And Clinical Outcomes Among Asthma Patients At King Saud Hospital, Saudi Arabia

The pharmacist is expected to play a vital role in chronic disease management. The present study aimed at developing an educational tool (booklet) and described a pharmacist--mediated structured medication counseling for asthmatic patients. It also determined the impact of pharmacist intervention on asthma patient’s knowledge, satisfaction, medication adherence, quality of life, clinical outcomes (peak expiratory flow rate, PEFR), and drug therapy-related problems (DTRPs). A randomized controlled trial compared the conventional routine counseling [control (n = 38)] with structured medication counseling provided by pharmacist [intervention (n = 37)] at the Outpatient Department of the Chest Clinic at King Saud Hospital, Qassim, Saudi Arabia

Vezetői kompetenciák elemzése a szervezeti változásokra való felkészültség alapján – A jordán egyetemi oktatókórházak példája

A vezetői készségek a szervezetek teljesítményét javíthatják, és közvetlenül hozzájárulhatnak a pozitív és fenntartható szervezeti változáshoz; igaz ez a dinamikusan változó egészségügyi környezetben is. Jelen kutatás egy nemzetközi mű-helymunka alapján létrejött keretrendszerben (Medical Leadership Competency Framework) vizsgálja, hogy a jordán ok-tatókórházak klinikai vezetőinek a kompetenciái mennyire jelennek meg a vezetői magatartásukban, illetve ezek milyen összefüggésben állnak a vizsgált vezetők demográfiai változóival. A kutatásban kvantitatív leíró vizsgálat készült: 110 kli-nikai vezető validált kérdőívet töltött ki, majd az adatokat többváltozós varianciaanalízissel elemezték a szerzők. A klinikai vezetői kompetenciák közül az integráció, az empátia és az innováció a jellemzően alkalmazott kompetencia. A vizsgált kompetenciák és a demográfiai változók (vezetők neme és életkora) között kapcsolatot találtak. Az életkori csoportok elemzéséből jól látható, hogy a klinikai vezetői készségek fejlesztése időigényes (főként a személyzet irányításával és a munkakörnyezettel kapcsolatos készségeké), viszont elengedhetetlen a környezeti, szervezeti változásra való felkészült-séghez

Challenges and opportunities in healthcare reforms in pre- and post-COVID-19 crisis : A case of Jordan

The COVID-19 crisis presented devastating effects on global healthcare, and Jordan was no exception. During such unforeseen challenges, attention shifts to healthcare leadership and management. However, limited research explored the leadership adjustment in Jordanian hospitals during the pandemic. Therefore, this study examined the challenges and opportunities that arose alongside the outbreak of the pandemic in Jordanian hospitals from the leaders’ perspectives. Semi-structured interviews were conducted with healthcare managers in three Jordanian hospitals, focusing on three key areas: healthcare reform integration, the impact of the COVID-19 crisis on hospital performance and healthcare quality, and management practices to handle the pandemic. Interviews were transcribed and thematically analyzed. Eleven hospital managers in seven administrative positions were involved. Three thematic outcomes were noted regarding the integration of healthcare reforms into the Jordanian healthcare system: sources of reforms, challenges of reform integration, and timely integration of the reforms. Positive impacts of the crisis on the Jordanian hospitals’ performance and quality of healthcare service delivery, which are considered opportunities, resulted in three thematic outcomes: infection control protocol, staff education, and patient management. On the other hand, three thematic concepts emerged as challenges of the COVID-19 crisis: workload, stress and fatigue, and staff shortages. Lastly, this study noted three thematic outcomes regarding management practices adopted by the Jordanian healthcare systems to deal with the pandemic: staff training, monitoring, and social support

Sustainable approach towards isolation of photosynthetic pigments from Spirulina and the assessment of their prooxidant and antioxidant properties

Carotenoids, chlorophyll and phycocyanin are three types of photosynthetic pigments found in Spirulina that differ in colour, composition, stability, solubility, and commercial importance. Such diversity of structures creates a challenge to extract these pigments simultaneously from the same batch of raw material in an efficient and sustainable manner. This study demonstrates that water can be successfully used as a single solvent together with combined (non)mechanical cell membrane disruption techniques (ultrasonication, centrifugation, freezing/thawing cycle) to extract these photosynthetic pigments from the same batch. This water-based approach delivers a significant improvement in isolating green pigments, which are often overlooked during extraction due to a preference for blue and yellow pigments. Chlorophyll was quantitatively converted to its stable derivatives to carry out a comparative analysis of antioxidant properties (DPPH, TEAC, FRAP), singlet oxygen production and intracellular activities (MTT, ROS assays) using Caco-2 cells

Adoption of antenatal care conversation mapping among health care providers in Saudi Arabia: Application of the diffusion innovation theory.

AIM: To measure the factors influencing on the adoption of antenatal care conversation mapping among health care providers in Riyadh (Saudi Arabia), using the diffusion innovation theory. METHODS: 88 healthcare providers (Riyadh) were recruited using a non-probability convenient sampling technique were trained on how to use a newly developed antenatal care conversation map. Data was collected by self-administrated questionnaire on health education services, adoption of conversation map and diffusion of innovation variables. The JMP statistical software from SAS version 14 was used to perform data analysis. RESULTS: Printable tools were most common as used by 72.7% of participants and 83.0% of them did not hear about conversation map. The total mean score of diffusion of innovation variables showed was in general high. The total mean score of relative advantage and observability was high in participants aged between 40 to less than 50 years, while the total mean score of compatibility, complexity, and trialability was high in participants aged from 50 years and more. Significant differences were obtained in both compatibility and trialability considering the health educators specialty, p = 0.03 and p = 0.027 respectively. The linear correlations between diffusion of innovation variables was significantly positive (p-value <0.01). CONCLUSION: All of diffusion of innovation variables were positive as per the opinion of the participants. Applying the conversation map on other health topics in Saudi Arabia and other Arabic-speaking countries is warranted. Measuring and evaluating the adoption rate of conversation mapping among health care providers on other health topics should be explored

Subject - specific - frequency - band for motor imagery EEG signal recognition based on common spatial spectral pattern

Over the last decade, processing of biomedical signals using machine learning algorithms has gained widespread attention. Amongst these, one of the most important signals is electroencephalography (EEG) signal that is used to monitor the brain activities. Brain-computer-interface (BCI) has also become a hot topic of research where EEG signals are usually acquired using non-invasive sensors. In this work, we propose a scheme based on common spatial spectral pattern (CSSP) and optimization of temporal filters for improved motor imagery (MI) EEG signal recognition. CSSP is proposed as it improves the spatial resolution while the temporal filter is optimized for each subject as the frequency band which contains most significant information varies amongst different subjects. The proposed scheme is evaluated using two publicly available datasets: BCI competition III dataset IVa and BCI competition IV dataset 1. The proposed scheme obtained promising results and outperformed other state-of-the-art methods. The findings of this work will be beneficial for developing improved BCI systems