Variable weight algorithm for convolutional neural networks and its applications to classification of seizure phases and types

作者：

Highlights：

• Convolutional Neural Networks can be improved in terms of the classification performance and robustness by using variable weight structures.

• Analysis of different data processing methods, models’ robustness and statistical properties.

• Comparative analysis of variable weight convolutional neural networks and other widely used machine learning techniques.

• Medical applications to the classification of seizure phases and types.

摘要

•Convolutional Neural Networks can be improved in terms of the classification performance and robustness by using variable weight structures.•Analysis of different data processing methods, models’ robustness and statistical properties.•Comparative analysis of variable weight convolutional neural networks and other widely used machine learning techniques.•Medical applications to the classification of seizure phases and types.

论文关键词：Variable weight convolutional neural networks,Machine learning,Seizure phase classification,Seizure type classification

论文评审过程：Received 13 March 2020, Revised 24 July 2021, Accepted 3 August 2021, Available online 4 August 2021, Version of Record 10 August 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108226