Self-Aware Anomaly-Detection for Epilepsy Monitoring on Low-Power Wearable Electrocardiographic Devices

Low-power wearable technologies offer a promising solution to pervasive epilepsy monitoring by removing the constraints concerning time and location, on one hand, and fulfilling long-term tracking, on the other hand. In the case of epileptic seizures, as the attacks infrequently occur, using an anomaly detection approach reduces the need to record long hours of data for each patient before detecting the successive coming seizures. In this work, by combining the concepts of self-aware system and anomaly detection, we propose an energy-efficient system to detect epileptic seizures on single-lead electrocardiographic signals, which is personalized after analyzing the first seizure of the patient. This system, then, uses a simple anomaly-detection model, whenever the model is deemed reliable, and uses a more complex model otherwise. We show that after the personalization, the number of patients, for which the method provides high sensitivity, can reach 26 out of 43 patients with the false alarm rate (FAR) of 4 alarms/day. Thus, the number of responders to the system is increased by 24%, while the FAR is only increased by one alarm/day, compared to the system that just uses the simple model. This benefit occurs while the system complexity decreases by 27.7% compared to the complex model. After adding the two-level (simple and complex) anomaly-detection, the complexity is tuned between 72.3% and 37.6% of the complex model. Similarly, the sensitivity is tuned between 66.5% and 60.3%.