Adaptive, Unlabeled and Real-time Approximate-Learning Platform (AURA) for Personalized Epileptic Seizure Forecasting

A high performance event detection system is all you need for some predictive studies. Here, we present AURA: an Adaptive forecasting model trained with Unlabeled, Real-time data using internally generated Approximate labels on-the-fly. By harnessing the correlated nature of time-series data, a pair of detection and prediction models are coupled together such that the detection model generates labels automatically, which are then used to train the prediction model. AURA relies on several simple principles and assumptions: (i) the performance of an event prediction/forecasting model in the target application remains below the performance of an event detection model, (ii) detected events are treated as weak labels and deemed reliable enough for online training of a predictive model, and (iii) system performance and/or system responsive feedback characteristics can be tuned for a subject-under-test. For example, in medical patient monitoring, this enables personalizing forecasting models. Seizure prediction is identified as an ideal test case of AURA, as pre-ictal brainwaves are patient-specific and tailoring models to individual patients can significantly improve forecasting performance. AURA is used to generate an individual forecasting model for 10 patients, showing an average relative improvement in sensitivity by 14.30% and reduction in false alarms by 19.61%. This paper presents a proof-of-concept for the feasibility of online transfer-learning on a stream of time-series neurophysiological data that pave the way towards a low-power neuromorphic neuromodulation system.