Öz
Automatic sleep staging is aimed within the scope of this paper. Sleep staging is a study by a sleep specialist. Since this process takes quite a long time and sleep is a method based on the knowledge and experience, it is inevitable for each person to show different results. For this, an automatic sleep staging method has been introduced. In the study, EEG (Electroencephalogram), EOG (Electrooculogram), EMG (Electromyogram) data recorded by PSG (Polysomnography) device for seven patients in Necmettin Erbakan University sleep laboratory were used. 81 different features were taken from the data in time and frequency environment. Also, PCA (Principal component analysis) and SFS (Sequential forward selection) feature selection methods were used. The classification success of the sleep phases in different machine learning methods was measured by using the received features. Linear D. (Linear Discriminant Analysis), Cubic SVM (Support vector machine), Weighted kNN (k nearest neighbor), Bagged Trees, ANN (Artificial neural network) were used as classifiers. System success was achieved with a 5 fold cross-validation method. Accuracy rates obtained were respectively 55.6%, 65.8%, 67%, 72.1%, and 69.1%.
Anahtar Kelimeler
Destekleyen Kurum
Tübitak
Proje Numarası
119E127
Teşekkür
Bu çalışma Tübitak tarafından "Türk Osas Hastalarında Cpap Değerini Etkileyen Polisomnografik Özelliklerin Belirlenmesi ve Optimum Cpap Değerinin Yapay Zekâ İle Tahmini" isimli ve 119E127 numaralı 1001 projesi kapsamında desteklenmektedir.
Kaynakça
- Abdulla, S., Diykh, M., Laft, R. L., Saleh, K., & Deo, R. C. (2019). Sleep EEG signal analysis based on correlation graph similarity coupled with an ensemble extreme machine learning algorithm. Expert Systems With Applications, vol. 138. DOI:https://doi.org/10.1016/j.eswa.2019.07.007
- Diykh, M., Li, Y., & Abdulla, S. (2020). EEG sleep stages identification based on weighted undirected complex networks. Computer Methods and Programs in Biomedicine, vol. 184. DOI: https://doi.org/10.1016/j.cmpb.2019.105116.
- Fan, Y. (2018). Research on Feature Extraction of EEG Signals using MSE-PCA and Sleep Staging. 2018 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Qingdao, pp. 1-5, DOI:10.1109/ICSPCC.2018.8567757.
- Hjorth, B. (1970). EEG analysis based on time domain properties. Electroencephalography and Clinical Neurophysiology, vol. 29, issue. 3, pp. 306–310. DOI: 10.1016/0013-4694(70)90143-4
- Huang, W., Guo, B., Shen, Y., Tang, X., Zhang, T., Li, D., & Zhonghui J. (2020). Sleep staging algorithm based on multichannel data adding and multifeature screening. Computer Methods and Programs in Biomedicine , vol.187. DOI: https://doi.org/10.1016/j.cmpb.2019.105253.
- Iber, C., Ancoli-Israel, S., Chesson, A. L., & Quan, S. L. (2007). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. American Academy of Sleep Medicine, Westchester, 2007.
- Jiang, D., Lu, Y., Ma, Y., & Wang, Y. (2019). Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement. Expert Systems With Applications, vol. 121, pp. 188–203. DOI:https://doi.org/10.1016/j.eswa.2018.12.023
- Liu, G. R., Lo, Y. L., Malik, J., Sheu, Y. C., & Wu, H. T. (2020). Diffuse to fuse EEG spectra – Intrinsic geometry of sleep dynamics for classification. Biomedical Signal Processing and Control, vol. 55. DOI: https://doi.org/10.1016/j.bspc.2019.101576.
- Savareh , B. A., Bashiri, A., Behmanesh, A., Meftahi, G. H., & Hatef, B. (2018). Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis. PeerJ. DOI 10.7717/peerj.5247
- Smith, L. I. (2002). A tutorial on Principal Components Analysis. http://www.sccg.sk/~haladova/principal_components.pdf
- Welch, P. D. (1967). The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms. IEEE Trans. Audio Electroacoust, vol. AU-15 (June 1967). pp. 70-73. DOI:10.1109/TAU.1967.1161901
- Whitney, A. W. (1971). A Direct Method of Nonparametric Measurement Selection. IEEE Transactions on Computers, vol. C-20, issue. 9, pp. 1100-1103. DOI: 10.1109/T-C.1971.223410
- Yücelbaş,Ş., Yücelbaş, C., Özşen, S., Tezel, G., Dursun, M., Küçüktürk, S., & Yosunkaya, Ş. (2015). Effect On The Classification Results of ECG Artifacts in Full Night Sleep EEG. The International Conference On Science, Ecology And Technology I, ICONSETE 2015, Vienna, Austria.
Öz
Automatic sleep staging is aimed within the scope of this paper. Sleep staging is a study by a sleep specialist. Since this process takes quite a long time and sleep is a method based on the knowledge and experience, it is inevitable for each person to show different results. For this, an automatic sleep staging method has been introduced. In the study, EEG (Electroencephalogram), EOG (Electrooculogram), EMG (Electromyogram) data recorded by PSG (Polysomnography) device for seven patients in Necmettin Erbakan University sleep laboratory were used. 81 different features were taken from the data in time and frequency environment. Also, PCA (Principal component analysis) and SFS (Sequential forward selection) feature selection methods were used. The classification success of the sleep phases in different machine learning methods was measured by using the received features. Linear D. (Linear Discriminant Analysis), Cubic SVM (Support vector machine), Weighted kNN (k nearest neighbor), Bagged Trees, ANN (Artificial neural network) were used as classifiers. System success was achieved with a 5 fold cross-validation method. Accuracy rates obtained were respectively 55.6%, 65.8%, 67%, 72.1%, and 69.1%.
Anahtar Kelimeler
Proje Numarası
119E127
Kaynakça
- Abdulla, S., Diykh, M., Laft, R. L., Saleh, K., & Deo, R. C. (2019). Sleep EEG signal analysis based on correlation graph similarity coupled with an ensemble extreme machine learning algorithm. Expert Systems With Applications, vol. 138. DOI:https://doi.org/10.1016/j.eswa.2019.07.007
- Diykh, M., Li, Y., & Abdulla, S. (2020). EEG sleep stages identification based on weighted undirected complex networks. Computer Methods and Programs in Biomedicine, vol. 184. DOI: https://doi.org/10.1016/j.cmpb.2019.105116.
- Fan, Y. (2018). Research on Feature Extraction of EEG Signals using MSE-PCA and Sleep Staging. 2018 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Qingdao, pp. 1-5, DOI:10.1109/ICSPCC.2018.8567757.
- Hjorth, B. (1970). EEG analysis based on time domain properties. Electroencephalography and Clinical Neurophysiology, vol. 29, issue. 3, pp. 306–310. DOI: 10.1016/0013-4694(70)90143-4
- Huang, W., Guo, B., Shen, Y., Tang, X., Zhang, T., Li, D., & Zhonghui J. (2020). Sleep staging algorithm based on multichannel data adding and multifeature screening. Computer Methods and Programs in Biomedicine , vol.187. DOI: https://doi.org/10.1016/j.cmpb.2019.105253.
- Iber, C., Ancoli-Israel, S., Chesson, A. L., & Quan, S. L. (2007). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. American Academy of Sleep Medicine, Westchester, 2007.
- Jiang, D., Lu, Y., Ma, Y., & Wang, Y. (2019). Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement. Expert Systems With Applications, vol. 121, pp. 188–203. DOI:https://doi.org/10.1016/j.eswa.2018.12.023
- Liu, G. R., Lo, Y. L., Malik, J., Sheu, Y. C., & Wu, H. T. (2020). Diffuse to fuse EEG spectra – Intrinsic geometry of sleep dynamics for classification. Biomedical Signal Processing and Control, vol. 55. DOI: https://doi.org/10.1016/j.bspc.2019.101576.
- Savareh , B. A., Bashiri, A., Behmanesh, A., Meftahi, G. H., & Hatef, B. (2018). Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis. PeerJ. DOI 10.7717/peerj.5247
- Smith, L. I. (2002). A tutorial on Principal Components Analysis. http://www.sccg.sk/~haladova/principal_components.pdf
- Welch, P. D. (1967). The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms. IEEE Trans. Audio Electroacoust, vol. AU-15 (June 1967). pp. 70-73. DOI:10.1109/TAU.1967.1161901
- Whitney, A. W. (1971). A Direct Method of Nonparametric Measurement Selection. IEEE Transactions on Computers, vol. C-20, issue. 9, pp. 1100-1103. DOI: 10.1109/T-C.1971.223410
- Yücelbaş,Ş., Yücelbaş, C., Özşen, S., Tezel, G., Dursun, M., Küçüktürk, S., & Yosunkaya, Ş. (2015). Effect On The Classification Results of ECG Artifacts in Full Night Sleep EEG. The International Conference On Science, Ecology And Technology I, ICONSETE 2015, Vienna, Austria.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Proje Numarası | 119E127 |
| Yayımlanma Tarihi | 5 Ekim 2020 |
| Yayımlandığı Sayı | Yıl 2020 Ejosat Özel Sayı 2020 (ICCEES) |
