On-Chip Epilepsy Detection: Where Machine Learning Meets 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 mainly rely on a physician interviewing the subject, which is not effective in the infant/children group. Moreover, patient-to-patient and age-to-age variations in seizure patterns make such detection particularly challenging. To expand the beneficiary group to even infants and also to effectively adapt to each patient, a wearable form factor, the patient-specific system with machine- learning is crucial. However, the wearable environment is challenging for circuit designers due to unstable skin-electrode interface, huge mismatch, and static/dynamic offset.

This lecture will cover the design strategies of patient-specific epilepsy detection System-on-Chip (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 one 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 different parameters will also be discussed. Moving on, we will cover the feature extraction and the patient-specific and patient-independent classification using the 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.