A Multistage Deep Belief Networks Application on Arrhythmia Classification

Year 2016, Volume: 4 Issue: Special Issue-1, 222 - 228, 25.12.2016

Gokhan Altan , Yakup Kutlu Novruz Allahverdı

https://izlik.org/JA85UD64AE

Abstract

An electrocardiogram (ECG) is a biomedical signal type that determines
the normality and abnormality of heart beats using the electrical activity of
the heart and has a great importance for cardiac disorders. The computer-aided
analysis of biomedical signals has become a fabulous utilization method over
the last years. This study introduces a multistage deep learning classification
model for automatic arrhythmia classification. The proposed model includes a
multi-stage classification system that uses ECG waveforms and the Second Order
Difference Plot (SODP) features using a Deep Belief Network (DBN) classifier
which has a greedy layer wise training with Restricted Boltzmann Machines
algorithm. The multistage DBN model classified the MIT-BIH Arrhythmia Database
heartbeats into 5 main groups defined by ANSI/AAMI standards. All ECG signals
are filtered with median filters to remove the baseline wander. ECG waveforms
were segmented from long-term ECG signals using a window with a length of 501
data points (R wave centered). The extracted waveforms and elliptical features
from the SODP are utilized as the input of the model.  The proposed DBN-based multistage arrhythmia
classification model has discriminated five types of heartbeats with a high accuracy
rate of 96.10%.

Keywords

Arrhythmia , Deep Belief Networks , DBN , Deep Learning , AAMI , ECG Waveform , Second Order Difference Plot , SODP

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