dc.contributor.advisor | Atalar, Abdullah | |
dc.contributor.author | Güngen, Murat Alp | |
dc.date.accessioned | 2019-10-04T14:03:21Z | |
dc.date.available | 2019-10-04T14:03:21Z | |
dc.date.copyright | 2019-09 | |
dc.date.issued | 2019-09 | |
dc.date.submitted | 2019-10-04 | |
dc.identifier.uri | http://hdl.handle.net/11693/52522 | |
dc.description | Cataloged from PDF version of article. | en_US |
dc.description | Thesis (Master's): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2019. | en_US |
dc.description | Includes bibliographical references (leaves 72-74). | en_US |
dc.description.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. | en_US |
dc.description.statementofresponsibility | by Murat Alp Güngen | en_US |
dc.format.extent | xvi, 103 leaves : charts (some color) ; 30 cm. | en_US |
dc.language.iso | English | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Neuromorphics | en_US |
dc.subject | ECG | en_US |
dc.subject | Arrhythmia | en_US |
dc.subject | Arrhythmia detection | en_US |
dc.title | An analog neuromorphic classifier chip for ECG arrhythmia detection | en_US |
dc.title.alternative | EKG'de aritmi tespiti için bir analog nöromorfik tanımlayıcı çip | en_US |
dc.type | Thesis | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.publisher | Bilkent University | en_US |
dc.description.degree | M.S. | en_US |
dc.identifier.itemid | B158120 | |