Deep learning from wristband sensor data: towards wearable, non-invasive seizure forecasting

Seizure forecasting may provide patients with timely warnings to adapt their daily activities and help clinicians deliver more objective, personalized treatments. While recent work has convincingly demonstrated that seizure risk assessment is possible, these early approaches relied largely on complex, often invasive setups including intracranial electrocorticography, implanted devices and multi-channel EEG, which limits translation of these methods to broad clinical application. To facilitate broader adaptation of seizure forecasting in clinical practice, non-invasive, easily applicable techniques that reliably assess seizure risk, in combination with clinical information, are crucial. Wristbands that continuously record physiological parameters, including electrodermal activity, body temperature, blood volume pressure and actigraphy, may afford monitoring of autonomous nervous system function and movement relevant for such a task, hence minimizing potential complications associated with invasive monitoring, and avoiding stigma associated with bulky external monitoring devices on the head. Here, we use deep learning to analyze long-term, multi-modal wristband sensor data from 50 patients with epilepsy (total duration \(>\)1400 hours) to assess its capability to distinguish preictal from interictal states. Prediction performance is assessed using area under the receiver operating charateristic (AUC) and improvement over chance (IoC) based on F1 scores. Using one- and two-dimensional convolutional neural networks, we identified better-than-chance predictability in out-of-sample test data in 60\% of the patients in leave-one-out and 43\% of patients in pseudo-prospective approaches. These results provide a step towards developing easier to apply, non-invasive methods for seizure risk assessments in patients with epilepsy.