ABSTRACT 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-to-patient and age-to-age variation on seizure pattern makes such detection particularly challenging; hence we need have a "personalized symptom detection". To expand the beneficiary group to even infants, and also 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 skin-electrode interface, huge mismatch, and static/dynamic offset. This webinar 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 single 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 webinar. We will conclude with interesting aspects and opportunities that lie ahead.