BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Canada/Pacific BEGIN:DAYLIGHT DTSTART:20180311T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20181104T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20181126T234310Z UID:5475BDBC-A868-4B9F-944F-81A219C32A56 DTSTART;TZID=Canada/Pacific:20180413T110000 DTEND;TZID=Canada/Pacific:20180413T115900 DESCRIPTION:Epilepsy is a severe and chronic neurological disorder that aff ects over 65 million people worldwide. Yet current seizure/epilepsy detect ion and treatment largely relies on a physician interviewing the subject\, which is not effective in infant/children group. Moreover\, patient-to-pa tient and age-to-age variation on seizure pattern makes such detection par ticularly challenging. To expand the beneficiary group to even infants\, a nd also to effectively adapt to each patient\, a wearable form-factor\, pa tient-specific system with machine learning is of crucial. However\, the w earable environment is challenging for circuit designers due unstable skin -electrode interface\, huge mismatch\, and static/dynamic offset.\n\nThis lecture will cover the design strategies of patient-specific epilepsy dete ction System-on-Chip. We will first explore the difficulties\, limitations and potential pitfalls in wearable interface circuit design\, and strateg ies to overcome such issues. Starting from a 1 op-amp instrumentation ampl ifier (IA)\, we will cover various IA circuit topologies and their key met rics to deal with offset compensation. Several state-of-the-art instrument ation amplifiers that emphasize on different parameters will also be discu ssed. Moving on\, we will cover the feature extraction and the patient-spe cific classification using Machine Learning technique. Finally\, an on-chi p epilepsy detection and recording sensor SoC will be presented\, which in tegrates all the components covered during the lecture. The lecture will c onclude with interesting aspects and opportunities that lie ahead.\n\nCo-s ponsored by: Sudip Shekhar\n\nSpeaker(s): Jerald Yoo\, \n\nRoom: MCLD-418\ , Bldg: Macleod\, Electrical & Computer Engineering\, 2356 Main Mall\, Van couver\, British Columbia\, Canada\, V6T1Z4 LOCATION:Room: MCLD-418\, Bldg: Macleod\, Electrical & Computer Engineering \, 2356 Main Mall\, Vancouver\, British Columbia\, Canada\, V6T1Z4 ORGANIZER:sudip@ece.ubc.ca SEQUENCE:2 SUMMARY:Distinguished Lecture: On-Chip Epilepsy Detection: Where Machine Le arning Meets Wearable\, Patient-Specific Seizure Monitoring URL;VALUE=URI:https://events.vtools.ieee.org/m/170655 X-ALT-DESC:Description: <br /><p>Epilepsy is a severe and chronic neurologi cal disorder that affects over 65 million people worldwide. Yet current se izure/epilepsy detection and treatment largely relies on a physician inter viewing the subject\, which is not effective in infant/children group. Mor eover\, patient-to-patient and age-to-age variation on seizure pattern mak es such detection particularly challenging. To expand the beneficiary grou p to even infants\, and also to effectively adapt to each patient\, a wear able form-factor\, patient-specific system with machine learning is of cru cial. However\, the wearable environment is challenging for circuit design ers due unstable skin-electrode interface\, huge mismatch\, and static/dyn amic offset.</p>\n<p>This lecture will cover the design strategies of pati ent-specific epilepsy detection System-on-Chip. We will first explore the difficulties\, limitations and potential pitfalls in wearable interface ci rcuit design\, and strategies to overcome such issues. Starting from a 1 o p-amp instrumentation amplifier (IA)\, we will cover various IA circuit to pologies and their key metrics to deal with offset compensation. Several s tate-of-the-art instrumentation amplifiers that emphasize on different par ameters will also be discussed. Moving on\, we will cover the feature extr action and the patient-specific classification using Machine Learning tech nique. Finally\, an on-chip epilepsy detection and recording sensor SoC wi ll be presented\, which integrates all the components covered during the l ecture. The lecture will conclude with interesting aspects and opportuniti es that lie ahead.</p> END:VEVENT END:VCALENDAR