TY - JOUR
T1 - A novel solution of enhanced loss function using deep learning in sleep stage classification
T2 - Predict and diagnose patients with sleep disorders
AU - Rajbhandari, Ereena
AU - Alsadoon, Abeer
AU - Prasad, P. W.C.
AU - Seher, Indra
AU - Nguyen, Tran Quoc Vinh
AU - Pham, Duong Thu Hang
N1 - Publisher Copyright:
© 2021, Springer Science+Business Media, LLC, part of Springer Nature.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/3
Y1 - 2021/3
N2 - Sleep stage classification is important to accurately predict and diagnose patients with sleep disorders. Though various deep learning approaches have been implemented to classify sleep classes, these consist limitations that impact the accuracy and processing time of the classification model. The aim of this research is to enhance the accuracy and minimize the training time of the deep learning classification model. The proposed system consists of One Dimensional Convolutional Neural Network (CNN) with enhanced loss function to improve the accuracy of scoring of five different sleep classes. Preprocessing, Feature Extraction and Classification are the main components of the proposed system. Initially, EEG signals are fed to an adaptive filter for preprocessing, in order to remove any noise in signal. Thereafter, feature is extracted through multiple convolutional and pooling layers, and finally the classification is done by fully connected layer using softmax activation with enhanced loss function. The proposed solution is tested on data samples from multiple datasets with five classes of Sleep classification. Based on the obtained results, the proposed solution has found to achieve an accuracy of 96.26% which is almost 4.2% higher than the state-of-the-art solution which is 92.76%. Furthermore, the processing time has been reduced by 11 milliseconds against the state-of-the-art solution. The proposed system focused on classifying sleep stages in five classes using EEG signals with deep learning approach. It enhances the loss function in order to minimize errors in the prediction of sleep classes and improves the accuracy of the model. Furthermore, the training speed of the model has also been reduced by applying batch normalization techniques inside the model. In the future, larger datasets of different sleep disorder patients with varying features can be used for training and implementing the proposed solution. The datasets can also be pre-processed using additional techniques to refine the data before feeding to the neural network model.
AB - Sleep stage classification is important to accurately predict and diagnose patients with sleep disorders. Though various deep learning approaches have been implemented to classify sleep classes, these consist limitations that impact the accuracy and processing time of the classification model. The aim of this research is to enhance the accuracy and minimize the training time of the deep learning classification model. The proposed system consists of One Dimensional Convolutional Neural Network (CNN) with enhanced loss function to improve the accuracy of scoring of five different sleep classes. Preprocessing, Feature Extraction and Classification are the main components of the proposed system. Initially, EEG signals are fed to an adaptive filter for preprocessing, in order to remove any noise in signal. Thereafter, feature is extracted through multiple convolutional and pooling layers, and finally the classification is done by fully connected layer using softmax activation with enhanced loss function. The proposed solution is tested on data samples from multiple datasets with five classes of Sleep classification. Based on the obtained results, the proposed solution has found to achieve an accuracy of 96.26% which is almost 4.2% higher than the state-of-the-art solution which is 92.76%. Furthermore, the processing time has been reduced by 11 milliseconds against the state-of-the-art solution. The proposed system focused on classifying sleep stages in five classes using EEG signals with deep learning approach. It enhances the loss function in order to minimize errors in the prediction of sleep classes and improves the accuracy of the model. Furthermore, the training speed of the model has also been reduced by applying batch normalization techniques inside the model. In the future, larger datasets of different sleep disorder patients with varying features can be used for training and implementing the proposed solution. The datasets can also be pre-processed using additional techniques to refine the data before feeding to the neural network model.
KW - Adaptive filtering
KW - Batch normalization
KW - Convolutional neural network (CNN)
KW - Deep learning
KW - Loss function
KW - Sleep stages classification
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U2 - 10.1007/s11042-020-10199-8
DO - 10.1007/s11042-020-10199-8
M3 - Article
AN - SCOPUS:85099045913
SN - 1380-7501
VL - 80
SP - 11607
EP - 11630
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 8
ER -