https://eprints.ncl.ac.uk/file_store/production/228275/D8E53094-D19C-4E63-8DDC-1A8732719703.pdf

Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations

Summary Sudden unexpected death in epilepsy (SUDEP) can affect individuals of any age, but is most common in younger adults (aged 20–45 years). Generalised tonic-clonic seizures are the greatest risk factor for SUDEP; most often, SUDEP occurs after this type of seizure in bed during sleep hours and the person is found in a prone position. SUDEP excludes other forms of seizure-related sudden death that might be mechanistically related (eg, death after single febrile, unprovoked seizures, or status epilepticus). Typically, postictal apnoea and bradycardia progress to asystole and death. A crucial element of SUDEP is brainstem dysfunction, for which postictal generalised EEG suppression might be a biomarker. Dysfunction in serotonin and adenosine signalling systems, as well as genetic disorders affecting cardiac conduction and neuronal excitability, might also contribute. Because generalised tonic-clonic seizures precede most cases of SUDEP, patients must be better educated about prevention. The value of nocturnal monitoring to detect seizures and postictal stimulation is unproven but warrants further study.

https://www.thelancet.com/pdfs/journals/laneur/PIIS1474-4422(16)30158-2.pdf

Sudden unexpected death in epilepsy: epidemiology, mechanisms, and prevention.

https://repository.usfca.edu/cgi/viewcontent.cgi?article=1091&context=nursing_fac

Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy

Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new sequencing machines, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of health care. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies published in the last five years. In this paper, we review and discuss big data application in four major biomedical subdisciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) public health informatics. Specifically, in bioinformatics, high-throughput experiments facilitate the research of new genome-wide association studies of diseases, and with clinical informatics, the clinical field benefits from the vast amount of collected patient data for making intelligent decisions. Imaging informatics is now more rapidly integrated with cloud platforms to share medical image data and workflows, and public health informatics leverages big data techniques for predicting and monitoring infectious disease outbreaks, such as Ebola. In this paper, we review the recent progress and breakthroughs of big data applications in these health-care domains and summarize the challenges, gaps, and opportunities to improve and advance big data applications in health care.

https://journals.sagepub.com/doi/pdf/10.4137/BII.S31559

Big Data Application in Biomedical Research and Health Care: A Literature Review

An integrated big data analytics-enabled transformation model: Application to health care

Periictal autonomic dysregulation is best studied using a "polygraphic" approach: electroencephalography ([EEG]), 3-channel electrocardiography [ECG], pulse oximetry, respiration, and continuous noninvasive blood pressure [BP]), which may help elucidate agonal pathophysiologic mechanisms leading to sudden unexpected death in epilepsy (SUDEP). A number of autonomic phenomena have been described in generalized tonic-clonic seizures (GTCS), the most common seizure type associated with SUDEP, including decreased heart rate variability, cardiac arrhythmias, and changes in skin conductance. Postictal generalized EEG suppression (PGES) has been identified as a potential risk marker of SUDEP, and PGES has been found to correlate with post-GTCS autonomic dysregulation in some patients. Herein, we describe a patient with a GTCS in whom polygraphic measurements were obtained, including continuous noninvasive blood pressure recordings. Significant postictal hypotension lasting >60 s was found, which closely correlated with PGES duration. Similar EEG changes are well described in hypotensive patients with vasovagal syncope and a similar vasodepressor phenomenon, and consequent cerebral hypoperfusion may account for the PGES observed in some patients after a GTCS. This further raises the possibility that profound, prolonged, and irrecoverable hypotension may comprise one potential SUDEP mechanism.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/epi.12251

Stephanie Chung

Papers

Significant postictal hypotension: expanding the spectrum of seizure-induced autonomic dysregulation.

Heart beats in the cloud: distributed analysis of electrophysiological ‘Big Data’ using cloud computing for epilepsy clinical research