{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T22:33:25Z","timestamp":1789079605252,"version":"build-2803163510"},"reference-count":51,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:00:00Z","timestamp":1768262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Automated sleep staging remains challenging due to the transitional nature of certain sleep stages, particularly N1. In this paper, we explore modulation spectrograms for automatic sleep staging to capture the transitional nature of sleep stages and compare them with conventional benchmark features, such as the Short-Time Fourier Transform (STFT) and the Continuous Wavelet Transform (CWT). We utilized a single-channel EEG (C4\u2013M1) from the DREAMT dataset with subject-independent validation. We stratify participants by the Apnea\u2013Hypopnea Index (AHI) into Normal, Mild, Moderate, and Severe groups to assess clinical generalizability. Our modulation-based framework significantly outperforms STFT and CWT in the Mild and Severe cohorts, while maintaining comparable high performance in the Normal and Moderate AHI groups. Notably, the proposed framework maintained robust performance in severe apnea cohorts, effectively mitigating the degradation observed in standard time\u2013frequency baselines. These findings demonstrate the effectiveness of modulation spectrograms for sleep staging while emphasizing the importance of medical stratification for reliable outcomes in clinical populations.<\/jats:p>","DOI":"10.3390\/bios16010056","type":"journal-article","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T09:27:12Z","timestamp":1768296432000},"page":"56","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Modulation-Based Feature Extraction for Robust Sleep Stage Classification Across Apnea-Based Cohorts"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7220-1271","authenticated-orcid":false,"given":"Unaza","family":"Tallal","sequence":"first","affiliation":[{"name":"School of Computing, Wichita State University, 1845 N, Wichita, KS 67208, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rupesh","family":"Agrawal","sequence":"additional","affiliation":[{"name":"Business Informatics Department, Northern Kentucky University, Highland Heights, KY 41099, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7191-4251","authenticated-orcid":false,"given":"Shruti","family":"Kshirsagar","sequence":"additional","affiliation":[{"name":"School of Computing, Wichita State University, 1845 N, Wichita, KS 67208, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chattu, V.K., Manzar, M.D., Kumary, S., Burman, D., Spence, D.W., and Pandi-Perumal, S.R. (2018). The global problem of insufficient sleep and its serious public health implications. Healthcare, 7.","DOI":"10.3390\/healthcare7010001"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Piwek, L., Ellis, D.A., Andrews, S., and Joinson, A. (2016). The Rise of Consumer Health Wearables: Promises and Barriers. PLoS Med., 13.","DOI":"10.1371\/journal.pmed.1001953"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s13534-023-00299-3","article-title":"Current status and prospects of automatic sleep stages scoring: Review","volume":"13","author":"Gaiduk","year":"2023","journal-title":"Biomed. Eng. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"584","DOI":"10.4236\/jbise.2014.78059","article-title":"A Sleep Scoring System Using EEG Combined Spectral and Detrended Fluctuation Analysis Features","volume":"7","author":"Farag","year":"2014","journal-title":"J. Biomed. Sci. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, D., Sun, J., She, Y., Cui, Y., Zeng, X., Lu, L., Tang, C., Xu, N., Chen, B., and Qin, W. (2023). A two-branch trade-off neural network for balanced scoring sleep stages on multiple cohorts. Front. Neurosci., 17.","DOI":"10.3389\/fnins.2023.1176551"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e40211","DOI":"10.2196\/40211","article-title":"Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study","volume":"25","author":"Haghayegh","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xu, Z., Yang, X., Sun, J., Liu, P., and Qin, W. (2020). Sleep Stage Classification Using Time-Frequency Spectra From Consecutive Multi-Time Points. Front. Neurosci., 14.","DOI":"10.3389\/fnins.2020.00014"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.1109\/TNSRE.2017.2721116","article-title":"DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG","volume":"25","author":"Supratak","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TNSRE.2021.3076234","article-title":"An Attention-Based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG","volume":"29","author":"Eldele","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.cmpb.2016.12.004","article-title":"A Comparative Review on Sleep Stage Classification Methods in Patients and Healthy Individuals","volume":"140","author":"Boostani","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1038\/s41562-019-0548-z","article-title":"Spectrotemporal modulation provides a unifying framework for auditory cortical asymmetries","volume":"3","author":"Flinker","year":"2019","journal-title":"Nat. Hum. Behav."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tiwari, A., Cassani, R., Kshirsagar, S., Tobon, D.P., Zhu, Y., and Falk, T.H. (2022). Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note. Sensors, 22.","DOI":"10.3390\/s22124579"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1109\/TNSRE.2019.2896659","article-title":"SeqSleepNet: End-to-end hierarchical recurrent neural network for automatic sleep staging","volume":"27","author":"Phan","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.1093\/jamia\/ocy131","article-title":"Expert-level sleep scoring with deep neural networks","volume":"25","author":"Biswal","year":"2018","journal-title":"J. Am. Med. Inform, Assoc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5229","DOI":"10.1038\/s41467-018-07229-3","article-title":"Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy","volume":"9","author":"Stephansen","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1016\/j.jacc.2016.11.069","article-title":"Sleep apnea: Types, mechanisms, and clinical cardiovascular consequences","volume":"69","author":"Javaheri","year":"2017","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.smrv.2016.07.002","article-title":"Prevalence of obstructive sleep apnea in the general population: A systematic review","volume":"34","author":"Senaratna","year":"2017","journal-title":"Sleep Med. Rev."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Guilleminault, C., and Bassiri, A. (2005). Clinical features and evaluation of obstructive sleep apnea-hypopnea syndrome and upper airway resistance syndrome. Principles and Practice of Sleep Medicine, Elsevier Saunders. [4th ed.].","DOI":"10.1016\/B0-72-160797-7\/50094-X"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"597","DOI":"10.5664\/jcsm.2172","article-title":"Rules for scoring respiratory events in sleep: Update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events","volume":"8","author":"Berry","year":"2012","journal-title":"J. Clin. Sleep Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1001\/jama.2020.3514","article-title":"Diagnosis and management of obstructive sleep apnea: A review","volume":"323","author":"Gottlieb","year":"2020","journal-title":"JAMA"},{"key":"ref_21","first-page":"109","article-title":"Reliability of the American Academy of Sleep Medicine rules for assessing sleep depth in clinical practice","volume":"11","author":"Younes","year":"2015","journal-title":"J. Clin. Sleep Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1515\/bmt-2016-0156","article-title":"Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network","volume":"63","author":"Zhang","year":"2018","journal-title":"Biomed. Eng. Tech."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"101204","DOI":"10.1016\/j.smrv.2019.07.007","article-title":"Automated sleep scoring: A review of the latest approaches","volume":"48","author":"Fiorillo","year":"2019","journal-title":"Sleep Med. Rev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1038\/s41746-020-0291-x","article-title":"Deep learning for automated sleep staging using instantaneous heart rate","volume":"3","author":"Sridhar","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_25","first-page":"1000","article-title":"Automatic sleep stage scoring using time\u2013frequency analysis and stacked sparse autoencoders","volume":"24","author":"Tsinalis","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.knosys.2013.02.014","article-title":"Automated EEG analysis of epilepsy: A review","volume":"45","author":"Acharya","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_27","unstructured":"Boashash, B. (2003). Time\u2013Frequency Signal Analysis and Processing: A Comprehensive Reference, Elsevier Academic Press."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"20170254","DOI":"10.1098\/rsta.2017.0254","article-title":"Analysis of time-varying signals using continuous wavelet and synchrosqueezed transforms","volume":"376","author":"Tary","year":"2018","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lekkas, G., Vrochidou, E., and Papakostas, G.A. (2025). Time\u2013Frequency Transformations for Enhanced Biomedical Signal Classification with Convolutional Neural Networks. BioMedInformatics, 5.","DOI":"10.3390\/biomedinformatics5010007"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/j.bbe.2020.01.010","article-title":"Automatic sleep stage classification using time\u2013frequency images of CWT and transfer learning using convolution neural network","volume":"40","author":"Jadhav","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"310290","DOI":"10.1155\/S1110865703305013","article-title":"Joint Acoustic and Modulation Frequency","volume":"2003","author":"Atlas","year":"2003","journal-title":"EURASIP J. Appl. Signal Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3394","DOI":"10.1121\/1.1624067","article-title":"Modulation spectra of natural sounds and ethological theories of auditory processing","volume":"114","author":"Singh","year":"2003","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1892","DOI":"10.1109\/TAFFC.2022.3188223","article-title":"Quality-aware bag of modulation spectrum features for robust speech emotion recognition","volume":"13","author":"Kshirsagar","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.bspc.2017.12.001","article-title":"A convolutional neural network for sleep stage scoring from raw single-channel EEG","volume":"42","author":"Sors","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1109\/TNSRE.2018.2813138","article-title":"A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series","volume":"26","author":"Chambon","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mousavi, S., Afghah, F., and Acharya, U.R. (2019). SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0216456"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Vilamala, A., Madsen, K.H., and Hansen, L.K. (2017, January 25\u201328). Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring. Proceedings of the 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), Tokyo, Japan.","DOI":"10.1109\/MLSP.2017.8168133"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1109\/JBHI.2023.3303197","article-title":"L-SeqSleepNet: Whole-cycle long sequence modelling for automatic sleep staging","volume":"27","author":"Phan","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, C., Qi, Y., Ding, X., Zhao, J., Sang, T., and Lee, M. (2022). A deep learning method approach for sleep stage classification with EEG spectrogram. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19106322"},{"key":"ref_40","unstructured":"Wang, W.K., Yang, J., Hershkovich, L., Jeong, H., Chen, B., Singh, K., Roghanizad, A.R., Shandhi, M.M.H., Spector, A.R., and Dunn, J. (2024, January 27\u201328). Addressing wearable sleep tracking inequity: A new dataset and novel methods for a population with sleep disorders. Proceedings of the Conference on Health, Inference, and Learning (CHIL), New York, NY, USA."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1016\/j.specom.2010.08.013","article-title":"Automatic speech emotion recognition using modulation spectral features","volume":"53","author":"Wu","year":"2011","journal-title":"Speech Commun."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1038\/nrn3731","article-title":"Neural processing of natural sounds","volume":"15","author":"Theunissen","year":"2014","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s10462-024-10721-6","article-title":"A review of convolutional neural networks in computer vision","volume":"57","author":"Zhao","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/78.650093","article-title":"Bidirectional recurrent neural networks","volume":"45","author":"Schuster","year":"1997","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_47","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from Imbalanced Data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1162\/tacl_a_00675","article-title":"A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation Practice","volume":"12","author":"Opitz","year":"2024","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Yang, J., Chen, Y., Yu, T., and Zhang, Y. (2025). LMCSleepNet: A Lightweight Multi-Channel Sleep Staging Model Based on Wavelet Transform and Multi-Scale Convolutions. Sensors, 25.","DOI":"10.3390\/s25196065"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"81","DOI":"10.5664\/jcsm.2350","article-title":"The American Academy of Sleep Medicine inter-scorer reliability program: Sleep stage scoring","volume":"9","author":"Rosenberg","year":"2013","journal-title":"J. Clin. Sleep Med."}],"container-title":["Biosensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-6374\/16\/1\/56\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T09:44:34Z","timestamp":1768297474000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-6374\/16\/1\/56"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,13]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["bios16010056"],"URL":"https:\/\/doi.org\/10.3390\/bios16010056","relation":{},"ISSN":["2079-6374"],"issn-type":[{"value":"2079-6374","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,13]]}}}