Background Epilepsy and hypertension are common chronic conditions, both showing high prevalence in older age groups. This review outlines current experimental and clinical evidence on both direct and indirect role of hypertension in epileptogenesis and discusses the principles of drug treatment in patients with hypertension and epilepsy. Methods We selected English-written articles on epilepsy, hypertension, stroke, and cerebrovascular disease until December, 2018. Results Renin-angiotensin system might play a central role in the direct interaction between hypertension and epilepsy, but other mechanisms may be contemplated. Large-artery stroke, small vessel disease and posterior reversible leukoencephalopathy syndrome are hypertension-related brain lesions able to determine epilepsy by indirect mechanisms. The role of hypertension as an independent risk factor for post-stroke epilepsy has not been demonstrated. The role of hypertension-related small vessel disease in adult-onset epilepsy has been demonstrated. Posterior reversible encephalopathy syndrome is an acute condition, often

The role of different factors in influencing the risk of seizures during multiple sclerosis (MS) is not known. To perform a systematic review and meta-analysis of risk factors for epilepsy during MS. Pubmed, Google scholar, and Scopus databases were searched. Articles published in English (1986-2016) were included. Nine studies were included (3 retrospective cohort and 6 case-control) enrolling 2845 MS patients (217 with epilepsy; 7.6%). MS patients with epilepsy had a younger age at onset compared to MS patients without seizures (difference in means = -5.42 years, 95% CI -7.19 to -3.66, p < 0.001). Mean EDSS value at inclusion tended to be higher in patients with epilepsy, without reaching statistical significance (difference in means = 0.45, 95% CI -0.01 to 0.91, p = 0.054). No differences were observed in sex distribution (OR = 0.94, 95% CI 0.51-1.72, p = 0.83) and clinical form (OR = 1.03, 95% CI 0.33-3.21, p = 0.96). Two studies evaluated presence and number of cortical lesions as a risk factor for epilepsy in MS using different MRI techniques: in one study, cortical lesions were more frequently observed in patients with epilepsy (OR = 7.06, 95% CI 2.39-20.8; p < 0.001). In the other, cortico-juxtacortical lesions were more frequently observed in patients with epilepsy (OR = 2.6, 95% CI 1.0-6.5; p = 0.047). Studies about risk factors for epilepsy during MS are heterogeneous. Compared to MS patients without seizures, patients with epilepsy have an earlier MS onset and a higher EDSS score after similar disease duration. Clinical form of MS and sex do not predict the appearance of seizures.

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Status epilepticus (SE) is a neurological and medical emergency, defined as a condition resulting either from the failure of the mechanisms responsible of seizure self-limitation or from the initiation of mechanisms which lead to atypically prolonged seizures. Further than death, SE can have long-term consequences, including neuronal injury, depending on the type, cause and duration of seizures with severe associated disabilities. In Europe, SE shows an incidence rate ranging about 9 to 40/100,000/y. In adults, mortality of patients with SE is ~30%, and even higher (up to 40%) in refractory status epilepticus. To date, etiology, duration, presence of comorbidity, level of consciousness, semiology and age are the main clinical predictors of SE outcome.

The diagnosis of psychogenic nonepileptic seizures (PNES) by means of electroencephalography (EEG) is not a trivial task during clinical practice for neurologists. No clear PNES electrophysiological biomarker has yet been found, and the only tool available for diagnosis is video EEG monitoring with recording of a typical episode and clinical history of the subject. In this paper, a data-driven machine learning (ML) pipeline for classifying EEG segments (i.e., epochs) of PNES and healthy controls (CNT) is introduced. This software pipeline consists of a semiautomatic signal processing technique and a supervised ML classifier to aid clinical discriminative diagnosis of PNES by means of an EEG time series. In our ML pipeline, statistical features like the mean, standard deviation, kurtosis, and skewness are extracted in a power spectral density (PSD) map split up in five conventional EEG rhythms (delta, theta, alpha, beta, and the whole band, i.e., 1–32 Hz). Then, the feature vector is fed into three different supervised ML algorithms, namely, the support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian network (BN), to perform EEG segment classification tasks for CNT vs. PNES. The performance of the pipeline algorithm was evaluated on a dataset of 20 EEG signals (10 PNES and 10 CNT) that was recorded in eyes-closed resting condition at the Regional Epilepsy Centre, Great Metropolitan Hospital of Reggio Calabria, University of Catanzaro, Italy. The experimental results showed that PNES vs. CNT discrimination tasks performed via the ML algorithm and validated with random split (RS) achieved an average accuracy of 0.97 ± 0.013 (RS-SVM), 0.99 ± 0.02 (RS-LDA), and 0.82 ± 0.109 (RS-BN). Meanwhile, with leave-one-out (LOO) validation, an average accuracy of 0.98 ± 0.0233 (LOO-SVM), 0.98 ± 0.124 (LOO-LDA), and 0.81 ± 0.109 (LOO-BN) was achieved. Our findings showed that BN was outperformed by SVM and LDA. The promising results of the proposed software pipeline suggest that it may be a valuable tool to support existing clinical diagnosis.

Ongoing research on diagnostic biomarkers of epilepsy is promising and future preclinical and clinical studies are warranted.

OTE is not a rare finding in unselected patients with TLE of unknown origin, provided that it is carefully searched. The absence of OTE in a large group of nonepileptic controls adds evidence to its epileptogenic role.

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