Abstract
In the inference process of existing deep learning models, it is usually necessary to process the input data level-wise, and impose a corresponding relational inductive bias on each level. This kind of relational inductive bias determines the theoretical performance upper limit of the deep learning method. In the field of sleep stage classification, only a single relational inductive bias is adopted at the same level in the mainstream methods based on deep learning. This will make the feature extraction method of deep learning incomplete and limit the performance of the method. In view of the above problems, a novel deep learning model based on hybrid relational inductive biases is proposed in this paper. It is called CCRRSleepNet. The model divides the single channel Electroencephalogram (EEG) data into three levels: frame, epoch, and sequence. It applies hybrid relational inductive biases from many aspects based on three levels. Meanwhile, multiscale atrous convolution block (MSACB) is adopted in CCRRSleepNet to learn the features of different attributes. However, in practice, the actual performance of the deep learning model depends on the nonrelational inductive biases, so a variety of matching nonrelational inductive biases are adopted in this paper to optimize CCRRSleepNet. The CCRRSleepNet is tested on the Fpz-Cz and Pz-Oz channel data of the Sleep-EDF dataset. The experimental results show that the method proposed in this paper is superior to many existing methods.
Highlights
In recent years, the performance of automatic sleep stage classification algorithms based on deep learning has gradually surpassed the performance of traditional machine learning methods and human experts
In practice, the actual performance of the deep learning model depends on the nonrelational inductive biases, so a variety of matching nonrelational inductive biases are adopted in this paper to optimize CCRRSleepNet
The performance of automatic sleep stage classification algorithms based on deep learning has gradually surpassed the performance of traditional machine learning methods and human experts
Summary
The performance of automatic sleep stage classification algorithms based on deep learning has gradually surpassed the performance of traditional machine learning methods and human experts. Deep learning methods often follow the end-to-end design concept, and emphasize the minimum prior representation and computational assumptions [1]. This minimum prior representation and computational assumption can be represented by inductive biases. The relational inductive biases are the key to determining the upper limit of the theoretical performance of the algorithm, and the nonrelational inductive biases determine the extent to which the actual performance reaches the theoretical performance. The connection of neurons reflects the relational inductive biases, such as convolution layer of local connection and recurrent layer of cross time step connection. The nonrelational inductive bias is reflected in other aspects, such as activation function, standardization, data augmentation, optimization algorithms, etc
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