ECG-based epileptic seizure prediction: Challenges of current data-driven models.

Objective: Up to a third of patients with epilepsy fail to achieve satisfactory seizure control. A reliable method of predicting seizures would alleviate psychological and physical impact. Dysregulation in heart rate variability (HRV) has been found to precede epileptic seizures and may serve as an extracerebral predictive biomarker.

The value of self-reported variables in epilepsy monitoring and management. A systematic scoping review.

Purpose: Self-reported records of seizure occurrences, seizure triggers and prodromal symptoms via paper or electronic tools are essential components of epilepsy management. Despite recent studies indicating that this information could hold important clinical value, the adoption of self-reported information in clinical practice is inconsistent and of uncertain value.

Methods: We performed a systematic scoping review of the literature following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Seizure forecasting with ultra long-term EEG signals.

Objective: The apparent randomness of seizure occurrence affects greatly the quality of life of persons with epilepsy. Since seizures are often phase-locked to multidien cycles of interictal epileptiform activity, a recent forecasting scheme, exploiting RNS data, is capable of forecasting seizures days in advance.

Methods: We tested the use of a bandpass filter to capture the universal mid-term dynamics enabling both patient-specific and cross-patient forecasting.

An educational intervention to optimize use of proton pump inhibitors in a Greek university hospital.

Background: Misuse of proton pump inhibitors (PPIs) is an alarming issue for patients and healthcare systems.

Methods: We conducted a 3-phase interventional, prospective study in a Greek university hospital. During Phase I, we collected data from patients' records to evaluate the appropriate use of PPIs.