{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:07:55Z","timestamp":1760144875174,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T00:00:00Z","timestamp":1716508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>One of the most essential components of human life is sleep. One of the first steps in spotting abnormalities connected to sleep is classifying sleep stages. Based on the kind and frequency of signals obtained during a polysomnography test, sleep phases can be separated into groups. Accurate classification of sleep stages from electroencephalogram (EEG) signals plays a crucial role in sleep disorder diagnosis and treatment. This study proposes a novel approach that combines feature selection techniques with convolutional neural networks (CNNs) to enhance the classification performance of sleep stages using EEG signals. Firstly, a comprehensive feature selection process was employed to extract discriminative features from raw EEG data, aiming to reduce dimensionality and enhance the efficiency of subsequent classification using mutual information (MI) and analysis of variance (ANOVA) after splitting the dataset into two sets\u2014the training set (70%) and testing set (30%)\u2014then processing it using the standard scalar method. Subsequently, a 1D-CNN architecture was designed to automatically learn hierarchical representations of the selected features, capturing complex patterns indicative of different sleep stages. The proposed method was evaluated on a publicly available EDF-Sleep dataset, demonstrating superior performance compared to traditional approaches. The results highlight the effectiveness of integrating feature selection with CNNs in improving the accuracy and reliability of sleep stage classification from EEG signals, which reached 99.84% with MI-50. This approach not only contributes to advancing the field of sleep disorder diagnosis, but also holds promise for developing more efficient and robust clinical decision support systems.<\/jats:p>","DOI":"10.3390\/a17060229","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T08:30:22Z","timestamp":1716539422000},"page":"229","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Employing a Convolutional Neural Network to Classify Sleep Stages from EEG Signals Using Feature Reduction Techniques"],"prefix":"10.3390","volume":"17","author":[{"given":"Maadh Rajaa","family":"Mohammed","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Anbar University, Ramadi 31001, Iraq"}]},{"given":"Ali Makki","family":"Sagheer","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Anbar University, Ramadi 31001, Iraq"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2456","DOI":"10.1109\/TBME.2022.3147187","article-title":"Sleep Transformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification","volume":"69","author":"Phan","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"01020","DOI":"10.1051\/e3sconf\/202343001020","article-title":"Sleep Track: Automated Detection and Classification of Sleep Stages","volume":"430","author":"Kumar","year":"2023","journal-title":"E3S Web Conf."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Almutairi, H., Hassan, G.M., and Datta, A. (2021). Classification of Obstructive Sleep Apnoea from Single-Lead ECG Signals Using Convolutional Neural and Long Short Term Memory Networks. Biomed. Signal Process. Control, 69.","DOI":"10.1016\/j.bspc.2021.102906"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1038\/s41572-018-0016-5","article-title":"REM sleep behaviour disorder","volume":"4","author":"Dauvilliers","year":"2018","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_5","unstructured":"Almutairi, H., Hassan, G., and Datta, A. (2023). Classification of sleep stages from EEG, EOG and EMG signals by SSNet. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"R1237","DOI":"10.1016\/j.cub.2017.10.026","article-title":"The Biology of REM Sleep","volume":"27","author":"Peever","year":"2017","journal-title":"Curr. Biol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Timplalexis, C., Diamantaras, K., and Chouvarda, I. (2019, January 28\u201330). Classification of Sleep Stages for Healthy Subjects and Patients with Minor Sleep Disorders. Proceedings of the IEEE 19th International Conference on Bioinformatics and Bioengineering, Athens, Greece.","DOI":"10.1109\/BIBE.2019.00068"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.cmpb.2019.04.032","article-title":"A review of automated sleep stage scoring based on physiological signals for the new millennia","volume":"176","author":"Faust","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sri, T.R., Madala, A.J., Duddukuru, S.L., Reddipalli, R., and Polasi, P.K. (2022, January 28\u201330). A Systematic Review on Deep Learning Models for Sleep Stage Classification. Proceedings of the 6th International Conference on Trends in Electronics and Informatics, Tirunelveli, India.","DOI":"10.1109\/ICOEI53556.2022.9776965"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.neucom.2020.05.085","article-title":"EEG Signal Processing with Separable Convolutional Neural Network for Automatic Scoring of Sleeping Stage","volume":"410","author":"Rivero","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yang, B., Zhu, X., Liu, Y., and Liu, H. (2021). A single-channel EEG based automatic sleep stage classification method leveraging deep one-dimensional convolutional neural network and hidden Markov model. Biomed. Signal Process. Control, 68.","DOI":"10.1016\/j.bspc.2021.102581"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jiang, X., Zhao, J., Du, B., and Yuan, Z. (2021, January 18\u201322). Self-supervised contrastive learning for EEG-based sleep staging. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), Virtual.","DOI":"10.1109\/IJCNN52387.2021.9533305"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yildirim, O., Baloglu, U.B., and Acharya, U.R. (2019). A deep learning model for automated sleep stages classification using PSG signals. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16040599"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"11607","DOI":"10.1007\/s11042-020-10199-8","article-title":"A novel solution of enhanced loss function using deep learning in sleep stage classification: Predict and diagnose patients with sleep disorders","volume":"80","author":"Rajbhandari","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108312","DOI":"10.1016\/j.jneumeth.2019.108312","article-title":"Deep convolutional neural network for classification of sleep stages from single-channel EEG signals","volume":"324","author":"Mousavi","year":"2019","journal-title":"J. Neurosci. Methods"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhu, T., Luo, W., and Yu, F. (2020). Convolution-and attention-based neural network for automated sleep stage classification. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17114152"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s41782-020-00101-9","article-title":"Analysis of EEG Signal to Classify Sleep Stages Using Machine Learning","volume":"4","author":"Santaji","year":"2020","journal-title":"Sleep Vigil."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9248410","DOI":"10.1155\/2018\/9248410","article-title":"Automatic sleep stage classification based on convolutional neural network and fine-grained segments","volume":"2018","author":"Cui","year":"2018","journal-title":"Complexity"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TBME.2018.2872652","article-title":"Joint classification and prediction CNN framework for automatic sleep stage classification","volume":"66","author":"Phan","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1016\/j.procs.2023.01.067","article-title":"A Deep Learning Approach to Automated Sleep Stages Classification Using Multi-Modal Signals","volume":"218","author":"Satapathy","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1188678","DOI":"10.3389\/fphys.2023.1188678","article-title":"SeriesSleepNet: An EEG time series model with partial data augmentation for automatic sleep stage scoring","volume":"14","author":"Lee","year":"2023","journal-title":"Front. Physiol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Masad, I., Alqudah, A., and Qazan, S. (2024). Automatic classification of sleep stages using EEG signals and convolutional neural networks. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0297582"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.neures.2022.09.009","article-title":"Sleep stage classification in EEG signals using the clustering approach based probability distribution features coupled with classification algorithms","volume":"188","author":"Li","year":"2023","journal-title":"Neurosci. Res."},{"key":"ref_24","first-page":"1947","article-title":"An evaluation of preprocessing techniques for text classification","volume":"16","author":"Kadhim","year":"2018","journal-title":"Int. J. Comput. Sci. Inf. Secur."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"784397","DOI":"10.3389\/fgene.2022.784397","article-title":"Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of infammatory bowel disease","volume":"13","author":"Kubinski","year":"2022","journal-title":"Front. Genet."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"20220278","DOI":"10.1515\/comp-2022-0278","article-title":"Data preprocessing impact on machine learning algorithm performance","volume":"13","author":"Amato","year":"2023","journal-title":"Open Comput. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"228","DOI":"10.2478\/bsrj-2021-0015","article-title":"The Proportion for Splitting Data into Training and Test Set for the Bootstrap in Classification Problems","volume":"12","author":"Vrigazova","year":"2021","journal-title":"Bus. Syst. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4832864","DOI":"10.1155\/2021\/4832864","article-title":"Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil","volume":"2021","author":"Nguyen","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_29","unstructured":"Muraina, I. (2022, January 13\u201315). Ideal Dataset Splitting Ratios in Machine Learning Algorithms: General Concerns for Data Scientists and Data Analysts. Proceedings of the 7th International Mardin Artuklu Scientific Researches Conference, Mardin, Turkey."},{"key":"ref_30","first-page":"66","article-title":"Comparison of the Influence of Standardization and Normalization of Data on the Effectiveness of Spongy Tissue Texture Classification","volume":"9","year":"2019","journal-title":"Inform. Autom. Pomiary W Gospod. I Ochr. Srodowiska"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Raju, V.N.G., Lakshmi, K.P., Jain, V.M., Kalidindi, A., and Padma, V. (2020, January 20\u201322). Study the Influence of Normalization\/Transformation process on the Accuracy of Supervised Classification. Proceedings of the 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India.","DOI":"10.1109\/ICSSIT48917.2020.9214160"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"de Amorima, L., Cavalcantia, G., and Cruz, R. (2022). The choice of scaling technique matters for classification performance. arXiv.","DOI":"10.1016\/j.asoc.2022.109924"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"109395","DOI":"10.1016\/j.asoc.2022.109395","article-title":"Analysis and improvements on feature selection methods based on artificial neural network weights","volume":"127","author":"Barbosa","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"21207","DOI":"10.1038\/s41598-023-48230-1","article-title":"A novel hybrid feature selection and ensemble-based machine learning approach for botnet detection","volume":"13","author":"Hossain","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1177\/0165551518770967","article-title":"Mutual information and sensitivity analysis for feature selection in customer targeting: A comparative study","volume":"45","author":"Barraza","year":"2019","journal-title":"J. Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.neucom.2022.09.101","article-title":"Feature selection using Decomposed Mutual Information Maximization","volume":"513","author":"Macedo","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_37","unstructured":"Pereira, G., dos Santos, M., and Carvalho, A. (2021). Evaluating Meta-Feature Selection for the Algorithm Recommendation Problem. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine Learning: Algorithms, Real-World Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","article-title":"Machine learning and deep learning","volume":"31","author":"Janiesch","year":"2021","journal-title":"Electron. Mark."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ahmad, J., Farman, H., and Jan, Z. (2019). Deep learning methods and applications. Deep Learning: Convergence to Big Data Analytics, Springer. Springer Briefs in Computer Science.","DOI":"10.1007\/978-981-13-3459-7_3"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"066053","DOI":"10.1088\/1741-2552\/ac4430","article-title":"A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface","volume":"18","author":"Mattioli","year":"2022","journal-title":"J. Neural Eng."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Azizjon, M., Jumabek, A., and Kim, W. (2020, January 19\u201321). 1D CNN based network intrusion detection with normalization on imbalanced data. Proceedings of the 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Fukuoka, Japan.","DOI":"10.1109\/ICAIIC48513.2020.9064976"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Qazi, E., Almorjan, A., and Zia, T. (2022). A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection. Appl. Sci., 12.","DOI":"10.3390\/app12167986"},{"key":"ref_44","first-page":"604","article-title":"1D-CNN based Model for Classification and Analysis of Network Attacks","volume":"12","author":"Singh","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1093\/icesjms\/fsab255","article-title":"Unlocking the potential of deep learning for marine ecology: Overview, applications, and outlook","volume":"79","author":"Goodwin","year":"2022","journal-title":"ICES J. Mar. Sci."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/6\/229\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:48:12Z","timestamp":1760107692000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/6\/229"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,24]]},"references-count":45,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["a17060229"],"URL":"https:\/\/doi.org\/10.3390\/a17060229","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2024,5,24]]}}}