IEEE Solid-State Circuits Magazine - Fall 2017 - 121

Prof. Jerald Yoo Visits SSCS New York Chapter

The IEEE New York Solid-State Circuits Society (SSCS) and Electron
Devices Joint Chapter held a Distinguished Lecture on 1 June 2017.
Prof. Jerald Yoo, Department of Electrical and Computer Engineering,
Singapore Institute for Neurotechnology, National University of Singapore, gave the lecture "On-Chip
Epilepsy Detection: Where Machine
Learning Meets Wearable, PatientSpecific Seizure Monitoring."
In his talk, Yoo first showed how
challenging a wearable environment
is, with 50/60-Hz noise, electrode dc
offset, 1/f noise, baseline drift, etc.
Starting from a basic instrumentation amplifier, he introduced design
strategies with detailed examples to
mitigate such challenges.
In the second part of the lecture,
Yoo showed how machine learning
can be used to achieve a patient-specific seizure detection system. Seizure detection is very challenging
because seizure pattern is very different from each patient; by adopting machine learning, the circuit can
"learn" each patient's seizure electrical
onset and monitor such events in real
time. When the seizure is detected,
Digital Object Identifier 10.1109/MSSC.2017.2746178
Date of publication: 16 November 2017

a raw electroencephalogram will
be recorded for further analysis, and
transcranial electrical stimulation
may be applied at the same time. He
also emphasized that, in this case,
deep learning may not be the best
choice because there are not enough
training sets.
Despite it being summer vacation, around 25 students attended
the seminar. After Yoo's lecture, students asked many questions and
engaged in a fruitful discussion.

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 the infant/children groups.
To expand the beneficiary group to infants, a wearable form-factor, patientspecific system design with machine
learning is crucial to mitigate and eventually prevent many chronic diseases.
However, the wearable environment
is challenging for circuit designers
due to its unstable skin-electrode interface. Wet and dry electrodes have
significantly different electrical characteristics that needs to be addressed.
Also, in such an environment, the trad-

eoff between available resources and
performance among the components,
both in the analog front end and the
digital back end, is crucial.
This lecture will cover the design
strategies of sensor interface circuits for such wearable sensors. We
will first explore the difficulties,
limitations, and potential pitfalls in
wearable interfaces and strategies
to overcome such issues. After that,
system-level considerations for better key metrics such as energy efficiency will be introduced. 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 different parameters
will also be discussed. We will then
see how the signal analysis part
impacts the analog interface circuit
design. Finally, an on-chip epilepsy
detection and recording sensor
system-on-chip will be presented,
which integrates all the components
covered during the lecture. The lecture will conclude with interesting aspects and opportunities that
lie ahead.
-Abira Sengupta

Prof. Jerald Yoo and a few of the attendees of the 1 June lecture.

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