TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG

2020 ◽  
Author(s):  
Akara Supratak ◽  
Yike Guo
Keyword(s):  
Deep Learning ◽  
Single Channel ◽  
Sleep Stage ◽  
Learning Model ◽  
Deep Learning Model ◽  
Sleep Stage Scoring

scholarly journals CCRRSleepNet: A Hybrid Relational Inductive Biases Network for Automatic Sleep Stage Classification on Raw Single-Channel EEG

2021 ◽  
Vol 11 (4) ◽  
pp. 456
Author(s):  
Wenpeng Neng ◽  
Jun Lu ◽  
Lei Xu
Keyword(s):  
Deep Learning ◽  
Single Channel ◽  
Sleep Stage ◽  
Learning Model ◽  
Actual Performance ◽  
Feature Extraction Method ◽  
Inductive Bias ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Deep Learning Model

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.

scholarly journals Auxiliary Equipment Detection in Marine Engine Rooms Based on Deep Learning Model

2021 ◽  
Vol 9 (9) ◽  
pp. 1006
Author(s):  
Jiahao Qi ◽  
Jundong Zhang ◽  
Qingyan Meng
Keyword(s):  
Deep Learning ◽  
Single Channel ◽  
Model Performance ◽  
Learning Model ◽  
Detection Accuracy ◽  
Auxiliary Equipment ◽  
Marine Engine ◽  
Engine Room ◽  
Pipeline Networks ◽  
Deep Learning Model

In the intelligent perception of the marine engine room, visual identification of auxiliary equipment is the prerequisite for defect recognition and anomaly detection. To improve the detection accuracy, this study presents an auxiliary equipment detector in the cabin based on a deep learning model. Owing to the compact layout of pipeline networks and the large disparity in the equipment scales, we initially adopted RetinaNet as the basic framework, and introduced the single channel plain architecture RepVGG as the feature extraction network to simplify the complexity and improve realtime detection. Secondly, the Neighbor Erasing and Transferring Mechanism (NETM) was applied in the feature pyramid to deal with more complicated scale variations. Then, the complete IoU (CIoU) regression loss function was used instead of smooth L1, and the DIoU Soft-NMS mechanism was proposed to alleviate the misdetection in congested cabins. Further, comparison experiments and ablation experiments were performed on the auxiliary equipment in a marine engine room (AEMER) dataset to validate the efficacy of these strategies on the model performance boost. Specifically, our model can correctly detect 93.44% of coolers, 100.00% of diesel engines, 60.26% of meters, 95.30% of pumps, 55.01% of reservoirs, 97.68% of oil separators, and 74.37% of valves in a practical cabin.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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