2026-10-19T01:10:02Zhttps://ebyu-rdlopen.rawdatalibrary.net/oai/request

oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/156542026-10-18T20:30:30Zopenairedrivercom_ebyu_10000col_ebyu_10001

Stable signal separation algorithm for ECG signalsIlgin, Fatih YavuzCelik, HüsamettinSevim, YusufBSSECGFICAICAJADEKICANMFAlgorithmsBlind source separationElectrocardiographyIntelligent systemsSeparationSilicate mineralsECG signalsElectrocardiogram signalHeart musclesHuman bodiesICA algorithmsInitial conditionsKernel independent component analysisSignal separation algorithmsIndependent component analysisElectrocardiogram (ECG) signal has biomedical signs showing the working activity of the heart muscle by human body. Being able to take these biomedical signs from human body is important for diagnosis. Although there are a lot of Independent Component Analysis (ICA) algorithms that separate ECG signals from noises in the literature, separation qualities of them depend on initial conditions. The aim of this study is to find an ICA algorithm giving the stable separation quality for different initial conditions. Experimental results show that Kernel Independent Component Analysis algorithm (KICA) provides more accurate and stable results than the other algorithms. © 2012 IEEE.2012Conference Objecthttps://rdlopen.rawdatalibrary.net/r/ebyu/15654https://doi.org/10.1109/inista.2012.6246990978-146731446-6enINISTA 2012 - International Symposium on INnovations in Intelligent SysTems and Applicationsinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/conferenceObjecthttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/15654