StApneaNet: A Deep Learning-Based Automatic Sleep Stage Adaptive Apnea Detection Network Using Single Channel EEG Signal
Sleep apnea is a serious sleep disorder that is characterized by abnormal pauses in breathing. It is related to neural activity that depends on the associated sleep stage of an apnea event. Electroencephalography (EEG) signal is widely used for neural activity analysis and can play a significant rol...
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2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10804148/ |
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| author | Suvasish Saha Shaikh Anowarul Fattah Mohammad Saquib |
| author_facet | Suvasish Saha Shaikh Anowarul Fattah Mohammad Saquib |
| author_sort | Suvasish Saha |
| collection | DOAJ |
| description | Sleep apnea is a serious sleep disorder that is characterized by abnormal pauses in breathing. It is related to neural activity that depends on the associated sleep stage of an apnea event. Electroencephalography (EEG) signal is widely used for neural activity analysis and can play a significant role along with sleep stage information in sleep apnea event detection. In this paper, a sleep stage adaptive deep neural network based apnea detection scheme, namely StApneaNet is proposed using single channel EEG signal. The proposed StApneaNet consists of two major blocks: joint model and decision model. In the joint model, multi-band EEG signals are used as input to a multi-kernel CNN block which gives time sequential inter-band related features through causal convolution. A residual squeeze and excitation based channel attention mechanism is then applied to the output feature channels which are further processed through a bi-directional long short term memory (Bi-LSTM) layer along with a temporal attention block. Both apnea prediction and sleep stage prediction are jointly optimized in the joint model that is initially pre-trained and later integrated as a non-trainable block with the trainable decision fusion block inside the decision model for the final apnea event detection. The whole trained network so built is capable of efficiently predicting apnea frames in the testing phase. Extensive experimentation is carried out on three publicly available EEG datasets for subjects-combined and subject-independent classification of apnea events and normal events of an apnea patient. It is found that the proposed StApneaNet offers 91.79% sensitivity, 88.13% specificity, and 89.96% accuracy for a 10-fold subjects-combined cross-validation scheme which are better than those of the existing methods. |
| format | Article |
| id | doaj-art-b5cd21613fcb430394ee90003f6b6c02 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-b5cd21613fcb430394ee90003f6b6c022025-08-20T02:40:03ZengIEEEIEEE Access2169-35362024-01-011219825019826110.1109/ACCESS.2024.351897210804148StApneaNet: A Deep Learning-Based Automatic Sleep Stage Adaptive Apnea Detection Network Using Single Channel EEG SignalSuvasish Saha0https://orcid.org/0009-0009-7172-2004Shaikh Anowarul Fattah1https://orcid.org/0000-0001-8090-2327Mohammad Saquib2https://orcid.org/0000-0002-9641-2397Department of Electrical and Electronic Engineering (EEE), Bangladesh University of Engineering and Technology, Dhaka, BangladeshDepartment of Electrical and Electronic Engineering (EEE), Bangladesh University of Engineering and Technology, Dhaka, BangladeshDepartment of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX, USASleep apnea is a serious sleep disorder that is characterized by abnormal pauses in breathing. It is related to neural activity that depends on the associated sleep stage of an apnea event. Electroencephalography (EEG) signal is widely used for neural activity analysis and can play a significant role along with sleep stage information in sleep apnea event detection. In this paper, a sleep stage adaptive deep neural network based apnea detection scheme, namely StApneaNet is proposed using single channel EEG signal. The proposed StApneaNet consists of two major blocks: joint model and decision model. In the joint model, multi-band EEG signals are used as input to a multi-kernel CNN block which gives time sequential inter-band related features through causal convolution. A residual squeeze and excitation based channel attention mechanism is then applied to the output feature channels which are further processed through a bi-directional long short term memory (Bi-LSTM) layer along with a temporal attention block. Both apnea prediction and sleep stage prediction are jointly optimized in the joint model that is initially pre-trained and later integrated as a non-trainable block with the trainable decision fusion block inside the decision model for the final apnea event detection. The whole trained network so built is capable of efficiently predicting apnea frames in the testing phase. Extensive experimentation is carried out on three publicly available EEG datasets for subjects-combined and subject-independent classification of apnea events and normal events of an apnea patient. It is found that the proposed StApneaNet offers 91.79% sensitivity, 88.13% specificity, and 89.96% accuracy for a 10-fold subjects-combined cross-validation scheme which are better than those of the existing methods.https://ieeexplore.ieee.org/document/10804148/Sleep apneasleep stageEEGcausal convolutionattentionBi-LSTM |
| spellingShingle | Suvasish Saha Shaikh Anowarul Fattah Mohammad Saquib StApneaNet: A Deep Learning-Based Automatic Sleep Stage Adaptive Apnea Detection Network Using Single Channel EEG Signal IEEE Access Sleep apnea sleep stage EEG causal convolution attention Bi-LSTM |
| title | StApneaNet: A Deep Learning-Based Automatic Sleep Stage Adaptive Apnea Detection Network Using Single Channel EEG Signal |
| title_full | StApneaNet: A Deep Learning-Based Automatic Sleep Stage Adaptive Apnea Detection Network Using Single Channel EEG Signal |
| title_fullStr | StApneaNet: A Deep Learning-Based Automatic Sleep Stage Adaptive Apnea Detection Network Using Single Channel EEG Signal |
| title_full_unstemmed | StApneaNet: A Deep Learning-Based Automatic Sleep Stage Adaptive Apnea Detection Network Using Single Channel EEG Signal |
| title_short | StApneaNet: A Deep Learning-Based Automatic Sleep Stage Adaptive Apnea Detection Network Using Single Channel EEG Signal |
| title_sort | stapneanet a deep learning based automatic sleep stage adaptive apnea detection network using single channel eeg signal |
| topic | Sleep apnea sleep stage EEG causal convolution attention Bi-LSTM |
| url | https://ieeexplore.ieee.org/document/10804148/ |
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