Pregled bibliografske jedinice broj: 847144
A Compressive Sensing Approach for ECG Classification
A Compressive Sensing Approach for ECG Classification // First International Workshop on Data Science Abstract Book / Lončarić, Sven ; Šmuc, Tomislav (ur.).
Zagreb, 2016. str. 27-29 (poster, međunarodna recenzija, sažetak, znanstveni)
CROSBI ID: 847144 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
A Compressive Sensing Approach for ECG Classification
Autori
Marasović, Tea ; Papić, Vladan
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni
Izvornik
First International Workshop on Data Science Abstract Book
/ Lončarić, Sven ; Šmuc, Tomislav - Zagreb, 2016, 27-29
Skup
First International Workshop on Data Science
Mjesto i datum
Zagreb, Hrvatska, 30.11.2016
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
ECG; compressive sensing; random forest
Sažetak
This paper addresses the problem of ECG signal variability using the theory of compressive sensing. Compressive sensing provides a novel framework for representing sparse or compressible signals, with a significantly reduced set of measurements than that needed by Nyquist rate and it can be implemented for cardiac arrhythmia detection. Furthermore, the paper applies random forest algorithm for reliable automatic classification of ECG signals.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split
