A Multistage Deep Learning Algorithm for Detecting Arrhythmia
Özet
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 MIT-BIH
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.
Koleksiyonlar
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