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Event-driven ECG classification using an open-source, LC-ADC based non-uniformly sampled dataset

author
Saeed, Maryam
Wang, Qingyuan
Märtens, Olev
Larras, Benoit
Frappe, Antoine
Cardiff, Barry
John, Deepu
statement of authorship
Maryam Saeed, Qingyuan Wang, Olev Märtens, Benoit Larras, Antoine Frappé, Barry Cardiff, Deepu John
source
2021 IEEE International Symposium on Circuits and Systems (ISCAS), Daegu, Korea May 22-28, 2021 : proceedings
location of publication
Danvers
publisher
IEEE
year of publication
2021
pages
5 p
conference name, date
IEEE International Symposium on Circuits and Systems (ISCAS) : “Smart Technology for an Intelligent Society”, May 22-28, 2021
conference location
Daegu, South Korea
url
https://doi.org/10.1109/ISCAS51556.2021.9401333
subject term
südame rütmihäired
tehisnärvivõrgud
andurid
kantavad seadmed
muundurid
Scopus
Conference Proceedings at Scopus
Article at Scopus
WOS
Article at WOS
kvartiil
Q3
category (general)
Engineering
category (sub)
Engineering. Electrical and electronic engineering
keyword
LC-ADC
level-crossing analog-to-digital converters
cardiac arrhythmia classification
artificial neural networks (ANN)
wearable sensors
event-driven data
ISSN
2158-1525
ISBN
978-1-7281-9201-7
notes
Bibliogr.: 15 ref
scientific publication
teaduspublikatsioon
classifier
3.1
TTÜ department
Thomas Johann Seebecki elektroonikainstituut
language
inglise
Uurimisrühm
Measurement electronics research group
Saeed, M., Wang, Q., Märtens, O., Larras, B., Frappé, A., Cardiff, B., John, D. Event-driven ECG classification using an open-source, LC-ADC based non-uniformly sampled dataset // 2021 IEEE International Symposium on Circuits and Systems (ISCAS), Daegu, Korea May 22-28, 2021 : proceedings. Danvers : IEEE, 2021. 5 p. https://doi.org/10.1109/ISCAS51556.2021.9401333