Single-Channel EEG Data Analysis Using a Multi-Branch CNN for Neonatal Sleep Staging
Neonatal sleep staging is crucial for understanding infant brain development and assessing neurological health. This study explores the optimal electrode configuration to reduce technical complexities and potential risks of causing skin irritation to neonates during data collection. A Multi-Branch C...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10433501/ |
| _version_ | 1797289906637111296 |
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| author | Hafza Ayesha Siddiqa Zhenning Tang Yan Xu Laishuan Wang Muhammad Irfan Saadullah Farooq Abbasi Anum Nawaz Chen Chen Wei Chen |
| author_facet | Hafza Ayesha Siddiqa Zhenning Tang Yan Xu Laishuan Wang Muhammad Irfan Saadullah Farooq Abbasi Anum Nawaz Chen Chen Wei Chen |
| author_sort | Hafza Ayesha Siddiqa |
| collection | DOAJ |
| description | Neonatal sleep staging is crucial for understanding infant brain development and assessing neurological health. This study explores the optimal electrode configuration to reduce technical complexities and potential risks of causing skin irritation to neonates during data collection. A Multi-Branch Convolutional Neural Network (CNN) is used to categorize neonatal sleep states based on single-channel Electroencephalography (EEG) data. The proposed model was trained and tested on 16803 30-second segments from 64 infants, all of whom were at post-menstrual age between 36 and 43 weeks at the Children’s hospital of Fudan University. A total of 74 extracted time and frequency domain linear and non-linear features are applied to improve the performance of a Multi-Branch CNN-based classification model. Additionally, using principal component analysis (PCA), feature selection and feature importance are also applied to identify the most important features. Notably, the F3 channel outperforms other single-channels and has accuracy and kappa values 74.27±0.80% and 0.61, respectively. Furthermore, a combination of four left-side electrodes yields slightly better classification accuracy (75.36±0.57%) compared to the four right-side electrodes (74.76±1.10%), with corresponding kappa values of 0.63 and 0.62, respectively. In addition to providing insights into optimal electrode configuration using single-channel and multi-channel EEG data, the results highlight the critical role played by specific EEG channels in sleep stage classification. This research has the potential to enhance neonate care and monitor sleep more effectively, enabling early detection of sleep-related abnormalities such as sleep disorders. Furthermore, this research effectively captures information from a single-channel, reducing computing load while maintaining commendable performance. Additionally, integrating time and frequency domain linear and non-linear features into neonatal sleep staging can enhance accuracy and provide a deeper insight into the complex dynamics and irregularities of newborn’s sleep patterns. |
| first_indexed | 2024-03-07T19:12:47Z |
| format | Article |
| id | doaj.art-f358df5bde1f4fe3912c7cb5bdf6f8cb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T19:12:47Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f358df5bde1f4fe3912c7cb5bdf6f8cb2024-03-01T00:00:32ZengIEEEIEEE Access2169-35362024-01-0112299102992510.1109/ACCESS.2024.336557010433501Single-Channel EEG Data Analysis Using a Multi-Branch CNN for Neonatal Sleep StagingHafza Ayesha Siddiqa0https://orcid.org/0000-0002-2890-7830Zhenning Tang1https://orcid.org/0009-0004-1098-6130Yan Xu2Laishuan Wang3Muhammad Irfan4https://orcid.org/0000-0002-1066-3604Saadullah Farooq Abbasi5Anum Nawaz6https://orcid.org/0000-0002-1148-0084Chen Chen7https://orcid.org/0000-0001-7587-3314Wei Chen8https://orcid.org/0000-0003-3720-718XDepartment of Electronic Engineering, School of Information Science and Technology, Center for Intelligent Medical Electronics, Fudan University, Shanghai, ChinaDepartment of Electronic Engineering, School of Information Science and Technology, Center for Intelligent Medical Electronics, Fudan University, Shanghai, ChinaDepartment of Neurology, Children’s Hospital of Fudan University, National Children’s Medical-Center, Shanghai, ChinaDepartment of Neonatology, Children’s Hospital of Fudan University, Shanghai, ChinaDepartment of Electronic Engineering, School of Information Science and Technology, Center for Intelligent Medical Electronics, Fudan University, Shanghai, ChinaDepartment of Electronic, Electrical and Systems Engineering, School of Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham, U.K.School of Information Science and Technology, Fudan University, Shanghai, ChinaHuman Phenome Institute, Fudan University, Shanghai, ChinaSchool of Biomedical Engineering, The University of Sydney, Camperdown, NSW, AustraliaNeonatal sleep staging is crucial for understanding infant brain development and assessing neurological health. This study explores the optimal electrode configuration to reduce technical complexities and potential risks of causing skin irritation to neonates during data collection. A Multi-Branch Convolutional Neural Network (CNN) is used to categorize neonatal sleep states based on single-channel Electroencephalography (EEG) data. The proposed model was trained and tested on 16803 30-second segments from 64 infants, all of whom were at post-menstrual age between 36 and 43 weeks at the Children’s hospital of Fudan University. A total of 74 extracted time and frequency domain linear and non-linear features are applied to improve the performance of a Multi-Branch CNN-based classification model. Additionally, using principal component analysis (PCA), feature selection and feature importance are also applied to identify the most important features. Notably, the F3 channel outperforms other single-channels and has accuracy and kappa values 74.27±0.80% and 0.61, respectively. Furthermore, a combination of four left-side electrodes yields slightly better classification accuracy (75.36±0.57%) compared to the four right-side electrodes (74.76±1.10%), with corresponding kappa values of 0.63 and 0.62, respectively. In addition to providing insights into optimal electrode configuration using single-channel and multi-channel EEG data, the results highlight the critical role played by specific EEG channels in sleep stage classification. This research has the potential to enhance neonate care and monitor sleep more effectively, enabling early detection of sleep-related abnormalities such as sleep disorders. Furthermore, this research effectively captures information from a single-channel, reducing computing load while maintaining commendable performance. Additionally, integrating time and frequency domain linear and non-linear features into neonatal sleep staging can enhance accuracy and provide a deeper insight into the complex dynamics and irregularities of newborn’s sleep patterns.https://ieeexplore.ieee.org/document/10433501/EEGsleep analysisneonatal sleep state classificationFFTDFAmultiscale fluctuation entropy |
| spellingShingle | Hafza Ayesha Siddiqa Zhenning Tang Yan Xu Laishuan Wang Muhammad Irfan Saadullah Farooq Abbasi Anum Nawaz Chen Chen Wei Chen Single-Channel EEG Data Analysis Using a Multi-Branch CNN for Neonatal Sleep Staging IEEE Access EEG sleep analysis neonatal sleep state classification FFT DFA multiscale fluctuation entropy |
| title | Single-Channel EEG Data Analysis Using a Multi-Branch CNN for Neonatal Sleep Staging |
| title_full | Single-Channel EEG Data Analysis Using a Multi-Branch CNN for Neonatal Sleep Staging |
| title_fullStr | Single-Channel EEG Data Analysis Using a Multi-Branch CNN for Neonatal Sleep Staging |
| title_full_unstemmed | Single-Channel EEG Data Analysis Using a Multi-Branch CNN for Neonatal Sleep Staging |
| title_short | Single-Channel EEG Data Analysis Using a Multi-Branch CNN for Neonatal Sleep Staging |
| title_sort | single channel eeg data analysis using a multi branch cnn for neonatal sleep staging |
| topic | EEG sleep analysis neonatal sleep state classification FFT DFA multiscale fluctuation entropy |
| url | https://ieeexplore.ieee.org/document/10433501/ |
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