An efficient ECG arrhythmia classification method based on Manta ray foraging optimization

作者：

Highlights：

• An efficient FS and ECG classification approach based on MRFO and SVM has proposed.

• A new morphological features descriptor has presented.

• MRFO-SVM has benchmarked on the MIT-BIH arrhythmia database.

• The MRFO-SVM performance is evaluated with seven well-known metaheuristics.

摘要

•An efficient FS and ECG classification approach based on MRFO and SVM has proposed.•A new morphological features descriptor has presented.•MRFO-SVM has benchmarked on the MIT-BIH arrhythmia database.•The MRFO-SVM performance is evaluated with seven well-known metaheuristics.

论文关键词：Electrocardiogram (ECG),Arrhythmia classification,Feature selection,Manta ray foraging optimization,Metaheuristics,Support vector machine

论文评审过程：Received 26 May 2020, Revised 1 April 2021, Accepted 26 April 2021, Available online 4 May 2021, Version of Record 17 May 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115131