A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trick

• URL: http://arxiv.org/abs/2110.15277v1
• Date: Wed, 27 Oct 2021 10:36:52 GMT
• Title: A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trick
• Authors: Yu Xue, Ziming Yuan and Adam Slowik
• Abstract summary: We propose an efficient five-sleep-consuming classification method using convolutional neural networks (CNNs) with a novel data processing trick. We make full use of genetic algorithm (GA), NASG, to search for the best CNN architecture. We verify convergence of our data processing trick and compare the performance of traditional CNNs before and after using our trick.
• Score: 4.365107026636095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the development of automatic sleep stage classification (ASSC) techniques, many classical methods such as k-means, decision tree, and SVM have been used in automatic sleep stage classification. However, few methods explore deep learning on ASSC. Meanwhile, most deep learning methods require extensive expertise and suffer from a mass of handcrafted steps which are time-consuming especially when dealing with multi-classification tasks. In this paper, we propose an efficient five-sleep-stage classification method using convolutional neural networks (CNNs) with a novel data processing trick and we design neural architecture search (NAS) technique based on genetic algorithm (GA), NAS-G, to search for the best CNN architecture. Firstly, we attach each kernel with an adaptive coefficient to enhance the signal processing of the inputs. This can enhance the propagation of informative features and suppress the propagation of useless features in the early stage of the network. Then, we make full use of GA's heuristic search and the advantage of no need for the gradient to search for the best architecture of CNN. This can achieve a CNN with better performance than a handcrafted one in a large search space at the minimum cost. We verify the convergence of our data processing trick and compare the performance of traditional CNNs before and after using our trick. Meanwhile, we compare the performance between the CNN generated through NAS-G and the traditional CNNs with our trick. The experiments demonstrate that the convergence of CNNs with data processing trick is faster than without data processing trick and the CNN with data processing trick generated by NAS-G outperforms the handcrafted counterparts that use the data processing trick too.

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