Automated detection of atrial fibrillation using long short-term memory network with RR interval signals

FAUST, Oliver, SHENFIELD, Alex, KAREEM, Murtadha, SAN, Tan Ru, FUJITA, Hamido and ACHARYA, U. Rajendra (2018). Automated detection of atrial fibrillation using long short-term memory network with RR interval signals. Computers in Biology and Medicine.

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Abstract

Atrial Fibrillation (AF), either permanent or intermittent (paroxysnal AF), increases the risk of cardioembolic stroke. Accurate diagnosis of AF is obligatory for initiation of effective treatment to prevent stroke. Long term cardiac monitoring improves the likelihood of diagnosing paroxysmal AF. We used a deep learning system to detect AF beats in Heart Rate (HR) signals. The data was partitioned with a sliding window of 100 beats. The resulting signal blocks were directly fed into a deep Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The system was validated and tested with data from the MIT-BIH Atrial Fibrillation Database. It achieved 98.51% accuracy with 10-fold cross-validation (20 subjects) and 99.77% with blindfold validation (3 subjects). The proposed system structure is straight forward, because there is no need for information reduction through feature extraction. All the complexity resides in the deep learning system, which gets the entire information from a signal block. This setup leads to the robust performance for unknown data, as measured with the blind fold validation. The proposed Computer-Aided Diagnosis (CAD) system can be used for long-term monitoring of the human heart. To the best of our knowledge, the proposed system is the first to incorporate deep learning for AF beat detection.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, Recurrent neural network, Heart rate, Atrial fibrillation
Research Institute, Centre or Group - Does NOT include content added after October 2018:	Materials and Engineering Research Institute > Engineering Research
Departments - Does NOT include content added after October 2018:	Faculty of Science, Technology and Arts > Department of Engineering and Mathematics
Identification Number:	https://doi.org/10.1016/j.compbiomed.2018.07.001
Depositing User:	Oliver Faust
Date Deposited:	23 Jul 2018 13:03
Last Modified:	18 Mar 2021 03:56
URI:	https://shura.shu.ac.uk/id/eprint/22042

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