Probabilistic grammar-based neuroevolution for physiological signal classification of ventricular tachycardia
作者：
Highlights：
• A deep neuroevolution system is presented.
• Our system is based on Probabilistic Model Building Genetic Programming approach.
• Deep neural networks discovered by the system can diagnose heart rhythm problems.
• Performance of the proposed network is evaluated on a physiological dataset.
• Our network can outperform several machine learning algorithms in diagnosis.
摘要
•A deep neuroevolution system is presented.•Our system is based on Probabilistic Model Building Genetic Programming approach.•Deep neural networks discovered by the system can diagnose heart rhythm problems.•Performance of the proposed network is evaluated on a physiological dataset.•Our network can outperform several machine learning algorithms in diagnosis.
论文关键词：Physiological signal classification,Heart disease,Neuroevolution,Probabilistic grammar,Genetic programming,Deep neural network
论文评审过程：Received 25 July 2018, Revised 16 May 2019, Accepted 5 June 2019, Available online 6 June 2019, Version of Record 14 June 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.06.012
