A Multistage Deep Learning Algorithm for Detecting Arrhythmia
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.
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