On-Chip Epilepsy Detection: Where Machine Learning Meets Wearable, Patient-Specific Wearable Healthcare.

Epilepsy is a severe and chronic neurological disorder that affects over 65 million people worldwide. Yet current seizure/epilepsy detection and treatment largely relies on a physician interviewing the subject, which is not effective in infant/children group. Moreover, patient-topatient and age-to-age variation on seizure pattern makes such detection particularly challenging. To expand the beneficiary group to even infants, and to effectively adapt to each patient, a wearable form-factor, patient-specific system with machine learning is of crucial. However, the wearable environment is challenging for circuit designers’ due unstable skinelectrode interface, huge mismatch, and static/dynamic offset. This lecture will cover the design strategies of patient-specific epilepsy detection System-onChip (SoC). We will first explore the difficulties, limitations and potential pitfalls in wearable interface circuit design, and strategies to overcome such issues. Starting from a 1 op-amp instrumentation amplifier (IA), we will cover various IA circuit topologies and their key metrics to deal with offset compensation. Several state-of-the-art instrumentation amplifiers that emphasize on different parameters will also be discussed. Moving on, we will cover the feature extraction and the patient-specific classification using Machine Learning technique. Finally, an on-chip epilepsy detection and recording sensor SoC will be presented, which integrates all the components covered during the lecture. The lecture will conclude with interesting aspects and opportunities that lie ahead.