An analog neuromorphic classifier chip for ECG arrhythmia detection
Author
Güngen, Murat Alp
Advisor
Atalar, Abdullah
Date
2019-10Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
Following Moore's Law, the increase in the availability of more processing power
alongside the development of algorithms that can use this power, electrocardiogram
(ECG) systems are now becoming a part of our daily lives. The analytical
detection of irregularities within the ECG scan, arrhythmias, is tricky due to the
variations in the signals that di er from people to people due to physiological
reasons. In order to overcome this problem, a two stage machine-learning based
time-domain algorithm is first developed and tested on MatLab using datasets
from the MIT - BIH Arrhythmia Database. The algorithm begins with the preprocessing
stage where seven features are extracted from the input ECG waveform.
These features are then moved onto the second classification stage where a
perceptron classifies the features as arrhythmic or normal. The algorithm was
then converted into an analog CMOS circuit using the XFAB XC06M3 fabrication
process on Cadence Virtuoso. Most of the operations in the preprocessing stage
were completed using operational transconductance amplifiers (OTAs). For the
classifier, the circuit uses analog
oating gate metal oxide semiconductor transistors
(FGMOS) to store the weights of the perceptron and a winner-take-all
current comparator for the activation function. Simulation results show that the
circuit works as intended with a power consumption of 290 W.