A Linearly Adaptive Sine–Cosine Algorithm with Application in Deep Neural Network for Feature Optimization in Arrhythmia Classification using ECG Signals

作者：

Highlights：

• Proposed novel Linearly Adaptive Sine–Cosine Algorithm (LA-SCA).

• Applied techniques improve macro and micro search abilities of original SCA.

• Performance is verified over a set of 23 benchmark problems.

• Feature extraction optimization in Deep Neural Network for Arrhythmia disease classification using ECG signals.

摘要

•Proposed novel Linearly Adaptive Sine–Cosine Algorithm (LA-SCA).•Applied techniques improve macro and micro search abilities of original SCA.•Performance is verified over a set of 23 benchmark problems.•Feature extraction optimization in Deep Neural Network for Arrhythmia disease classification using ECG signals.

论文关键词：Sine–Cosine Algorithm,Linear Adaptive operator,Opposition based Learning,Deep Neural Network (DNN),Electrocardiogram (ECG)

论文评审过程：Received 14 November 2021, Revised 2 February 2022, Accepted 8 February 2022, Available online 16 February 2022, Version of Record 25 February 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.108411