A Multistage Deep Learning Algorithm for Detecting Arrhythmia

dc.contributor.author	ALLAHVERDİ, Novruz
dc.contributor.author	KUTLU, Yakup
dc.contributor.author	ALTAN, Gökhan
dc.date.accessioned	2019-07-10T11:56:44Z
dc.date.available	2019-07-10T11:56:44Z
dc.date.issued	2017-01
dc.identifier.other	DOI: 10.1109/CAIS.2018.8441942
dc.identifier.uri	https://hdl.handle.net/20.500.12498/1105
dc.description.abstract	Deep Belief Networks (DBN) is a deep learning algorithm that has both greedy layer-wise unsupervised and supervised training. Arrhythmia is a cardiac irregularity caused by a problem of the heart. In this study, a multi-stage DBN classification is proposed for achieving the efficiency of the DBN on arrhythmia disorders. Heartbeats from the MITBIH Arrhythmia database are classified into five groups which are recommended by AAMI. The Wavelet packet decomposition, higher order statistics, morphology and Discrete Fourier transform techniques were utilized to extract features. The classification performances of the DBN are 94.15%, 92.64%, and 93.38%, for accuracy, sensitivity, and selectivity, respectively.
dc.language.iso	en	en_US
dc.publisher	IEEE	en_US
dc.subject	Deep Learning	en_US
dc.subject	Deep Belief Network	en_US
dc.subject	Arrhythmia	en_US
dc.subject	ECG	en_US
dc.subject	Deep Learning
dc.subject	ECG
dc.subject	Arrhythmia
dc.subject	Deep Belief Network
dc.title	A Multistage Deep Learning Algorithm for Detecting Arrhythmia	en_US
dc.type	Konferans Bildirisi	en_US