As beneficial as they are, health monitors for conditions like high
blood pressure, arrhythmia, and epilepsy can be uncomfortable and
inconvenient due to all of their protruding wires. This opens up an
opportunity for designers of wearable healthcare applications.
“Wearable electronics are needed for proactive healthcare,” said Dr.
Jerald Yoo, an associate professor in the Department of Electrical
Engineering and Computer Science at the Masdar Institute of Science and
Technology in Abu Dhabi. The ability to detect and treat chronic disease
early can be a powerful countermeasure. However, this effort calls for
the collection and monitoring of voluminous amounts of data, which is
where wearable healthcare devices come in.
For wearable (and wireless) healthcare devices to be successful, Yoo
noted, they must be energy efficient, minimally obtrusive, and
disposable. Yoo discussed the role of energy-efficient circuits in
wearable healthcare applications during a recent talk to Cadence
employees at the company’s San Jose headquarters.
Healthcare wearables: what goes into the design?
According to the World Health Organization, about 50 million people
worldwide have epilepsy. Currently, diagnosing this severe neurological
disorder involves doctors interviewing the patient and administering an
electroencephalogram (EEG) test, said Yoo. But these methods are hardly
conclusive—what is really needed is continuous monitoring, he said.
During his talk, Yoo discussed the challenges and techniques to
designing biomedical circuitry. As an example, he highlighted a
closed-loop seizure detection microsystem. Creating such a system calls
for several key components.
First comes the platform. Here, Yoo considers the introduction of
printed fabric circuit boards about six years ago to be quite the
revelation. Direct screen-printing of conductive ink on fabric has made
many wearable applications possible. The technology also provides an
alternative to wet electrodes (which can trigger skin sensitivities if
worn for long periods) and dry electronics (which have high electrode
impedance and, thus, more noise). Designers creating fabric circuit
boards must address a number of challenges, including pad number limits
and issues such as heat protection, static and dynamic parameter
variation, and high impedance.
Next is the sensor I/F circuit—basically, these designs should use
low-noise, energy-efficient implementation circuits. Yoo noted that
here, it’s important to have a dedicated DC server loop to remove the
electrode offset. Since the servo loop itself elevates noise, Yoo has
worked with his students to create a design prototype of a wearable EEG
that includes a 500Hz chopper at the servo loop for better noise
efficiency.
The digital backend is also critical, providing patient-specific
classification and requiring energy efficiency. In this area, there are
some distinct EEG seizure detection challenges to be aware of. Namely,
intra-patient age-to-age EEG variations and spatial EEG variations are
unexpected outcomes. “Many times, the pattern for seizure and
non-seizure is very different. The seizure pattern from patient A has
almost no correlation to patient B. Other chronic diseases have similar
issues,” he explained.
Machine learning addresses variations
Yoo has found that the introduction of machine learning via support
vector machines (SVMs) provides a way to resolve these unexpected
outcomes. There are two options here: linear SVM (LSVM), which requires
limited seizure patterns but offers moderate classification accuracy,
and non-linear SVM (NLSVM), which requires sufficient seizure patterns
and has high classification accuracy. In his design prototype, Yoo’s
choice was to use two LSVMs, one trained for sensitivity and the other
trained for specificity. Using this approach in a single system, he
found accuracy rates of 95% for sensitivity and 98% for specificity
detection performance.
Finally, there is the system-level consideration, which, in this
case, consists of the seizure detection system based on a wirelessly
powered electrocardiogram (ECG). Yoo and his students used a fully
integrated EEG SoC consisting of a 1.8V analog front-end and 1.0V
digital backend with 16 channels, SVM and simulation, scalable EEG
processing, and machine learning for patient-specific seizure detection.
All of their work was implemented using Cadence tools. “System-level
consideration for circuit design is very important—you don’t want to
burn all of the power from the analog front end and vice versa,” Yoo
noted. The next step for Yoo’s work is to test the wearable EEG design
on patients.
http://semiengineering.com/the-role-of-energy-efficient-circuits-in-wearable-healthcare-applications/
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