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D'Souza, W.

Publications and source records attributed to D'Souza, W..

2 recordsLinked to original sources

Critical slowing as a biomarker for seizure susceptibility

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. Theoretical interpretations considering the brain as a dynamical system would suggest that prior to a seizure recorded brain signals may exhibit critical slowing, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we found key signatures of critical slowing prior to seizures. Signals related to a critically slowing process fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing is a reliable indicator that could be used in seizure forecasting algorithms.

neuroscience

Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy

Epilepsy diagnosis can be costly, time-consuming and not uncommonly inaccurate. The reference standard diagnostic monitoring is continuous video-EEG monitoring, ideally capturing all events or concordant interictal discharges. Automating EEG data review would save time and resources, thus enabling more people to receive reference standard monitoring and also potentially herald a more quantitative approach to therapeutic outcomes. There is substantial research into automated detection of seizures and epileptic activity from EEG. However, automated detection software is not widely used in the clinic; and, despite numerous published algorithms, few methods have regulatory approval for detecting epileptic activity from EEG.\n\nThis study reports on a deep learning algorithm for computer-assisted EEG review. Deep, convolutional neural networks were trained to detect epileptic discharges using a pre-existing dataset of over 6000 labelled events in a cohort of 103 patients with idiopathic generalized epilepsy (IGE). Patients underwent 24-hour ambulatory outpatient EEG, and all data was curated and confirmed independently by two epilepsy specialists (Seneviratne et al, 2016). The resulting automated detection algorithm was then used to review diagnostic scalp-EEG for seven patients (four with IGE and three with events mimicking seizures) to validate performance in a clinical setting.\n\nThe automated detection algorithm showed state-of-the-art performance for detecting epileptic activity from clinical EEG, with mean sensitivity of >95% and corresponding mean false positive rate of 1 detection per minute. Importantly, diagnostic case studies showed that the automated detection algorithm reduced human review time by 80%-99%, without compromising event detection or diagnostic accuracy. The presented results demonstrate that computer-assisted review can increase the speed and accuracy of EEG assessment and has the potential to greatly improve therapeutic outcomes.

neuroscience