Electrical and Electronic Engineering, Imperial College London
Doi
Abstract
Truly Wearable EEG (WEEG) can be considered as the future of ambulatory EEG
units, which are the current standard for long-term EEG monitoring. Replacing
these short lifetime, bulky units with long-lasting, miniature and wearable devices
that can be easily worn by patients will result in more EEG data being collected for
extended monitoring periods. This thesis presents three new fabricated systems, in
the form of Application Specific Integrated Circuits (ASICs), to aid the diagnosis of
epilepsy and sleep disorders by detecting specific clinically important EEG events
on the sensor node, while discarding background activity. The power consumption
of the WEEG monitoring device incorporating these systems can be reduced since
the transmitter, which is the dominating element in terms of power consumption,
will only become active based on the output of these systems.
Candidate interictal activity is identified by the developed analog-based interictal
spike selection system-on-chip (SoC), using an approximation of the Continuous
Wavelet Transform (CWT), as a bandpass filter, and thresholding. The spike
selection SoC is fabricated in a 0.35 μm CMOS process and consumes 950 nW.
Experimental results reveal that the SoC is able to identify 87% of interictal spikes
correctly while only transmitting 45% of the data.
Sections of EEG data containing likely ictal activity are detected by an analog
seizure selection SoC using the low complexity line length feature. This SoC is
fabricated in a 0.18 μm CMOS technology and consumes 1.14 μW. Based on experimental
results, the fabricated SoC is able to correctly detect 83% of seizure
episodes while transmitting 52% of the overall EEG data.
A single-channel analog-based sleep spindle detection SoC is developed to aid
the diagnosis of sleep disorders by detecting sleep spindles, which are characteristic
events of sleep. The system identifies spindle events by monitoring abrupt changes
in the input EEG. An approximation of the median frequency calculation, incorporated
as part of the system, allows for non-spindle activity incorrectly identified
by the system as sleep spindles to be discarded. The sleep spindle detection SoC
is fabricated in a 0.18 μm CMOS technology, consuming only 515 nW. The SoC
achieves a sensitivity and specificity of 71.5% and 98% respectively.Open Acces