Seizure unpredictability is rated as one of the most challenging aspects of living with epilepsy. Seizure likelihood can be influenced by a range of environmental and physiological factors that are difficult to measure and quantify.

The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, key signatures of critical slowing down prior to seizures was monitored.

Noninvasive wearable devices have great potential to aid the management of epilepsy, but these devices must have robust signal quality, and patients must be willing to wear them for long periods of time. Automated machine learning classification of wearable biosensor signals requires quantitative measures of signal quality to automatically reject poor‐quality or corrupt data segments.

Video‐electroencephalographic (EEG) monitoring is an essential tool in epileptology, conventionally carried out in a hospital epilepsy monitoring unit. Due to high costs and long waiting times for hospital admission, coupled with technological advances, several centers have developed and implemented video‐EEG monitoring in the patient’s home (home video‐EEG telemetry [HVET]).

There is an increasing interest in the effect of IED burden on acute and long-term cognitive function, and so semiautomated IED detection methods used for calculating burden may be of clinical utility. Here, we report on the performance of an algorithm for computer-assisted electroencephalography (EEG) review in patients with IGE for estimating IED event onset and offset, and predicting IED burden.

Manual scoring of polysomnography data is labor-intensive and time-consuming, and most existing software does not account for subjective differences and user variability. Therefore, we evaluated a supervised machine learning algorithm, SomnivoreTM, for automated wake–sleep stage classification.

Clusters are reported to be a marker of antiepileptic drug resistance. Additionally, seizure clustering has been found to be associated with increased morbidity and mortality. However, there are no statistical methods described in the literature to delineate bursting phenomenon in epileptic seizures.

This research shows that the transition to seizure is not a sudden phenomenon, but is instead a slow process that is characterized by the progressive loss of neuronal network resilience.

Seizures follow stereotypical pathways from start to finish, which are highly stereotypical for individuals. This research may eventually be used to guide personalised treatment strategies, like choosing anti-epileptic drugs, or designing counter-stimulation for brain implant devices.

The Lancet Neurology has recently published research from Seer team members, Pip Karoly and Dean Freestone. This research shows that daily, weekly and monthly cycles of seizures are common in men and women and can impact epilepsy treatment.

Seer team members, Dean Freestone and Pip Karoly, have published research outlining a web platform for developing seizure forecasting tools. Machine learning and cloud computing are unlocking new possibilities for developing individualised forecasts of seizure likelihood.

This article shows that different seizure populations have distinct and consistent postictal behaviors. The existence of multiple populations in some patients has implications for seizure management and forecasting, whereas the distinct postictal behaviors may have implications for sudden unexpected death in epilepsy (SUDEP) prediction and prevention.

The estimation of seizure frequency is a cornerstone of clinical management of epilepsy and the evaluation of new therapies. Current estimation approaches are significantly limited by several factors. Comparing patient diaries and objective estimates (through both inpatient video-EEG monitoring of and long-term ambulatory EEG studies) reveal that patients document seizures inaccurately. So far, few practical alternative methods of estimation have been available.

Seizure prediction has made important advances over the last decade, with the recent demonstration that prospective seizure prediction is possible, though there remain significant obstacles to broader application. In this review, we will describe insights gained from long-term trials, with the aim of identifying research goals for the next decade.

Every morning you wake up and check the weather app on your smartphone, to see if it will rain. If the forecast probability is high enough, let’s say 80 per cent, you decide to bring an umbrella to work.
Now, imagine waking up and not knowing if you will have a seizure that day. You might not be able to go to work at all. This is the uncertainty faced by people with epilepsy every day. An app providing a daily seizure forecast would be life changing.

Seizure prediction has made important advances over the last decade, with the recent demonstration that prospective seizure prediction is possible, though there remain significant obstacles to broader application. In this review, we will describe insights gained from long-term trials, with the aim of identifying research goals for the next decade.

In 2016 Kaggle held an open-source seizure prediction competition. A total of 478 teams entered and their efforts provided hope for the millions of patients with medically refractory epilepsy that another solution exists to treat seizures.

Deep learning technology is uniquely suited to analyse neurophysiological signals such as the electroencephalogram (EEG) and local field potentials (LFP) and promises to outperform traditional machine-learning based classification and feature extraction algorithms.

This review highlights recent developments in the field of epileptic seizure prediction. We argue that seizure prediction is possible; however, most previous attempts have used data with an insufficient amount of information to solve the problem.

Seizure prediction would be clinically useful in patients with epilepsy and could improve safety, increase independence, and allow acute treatment. We did a multicentre clinical feasibility study to assess the safety and efficacy of a long-term implanted seizure advisory system designed to predict seizure likelihood and quantify seizures in adults with drug-resistant focal seizures.