{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T05:04:52Z","timestamp":1779944692868,"version":"3.53.1"},"reference-count":53,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T00:00:00Z","timestamp":1779753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007518","name":"NDSU Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007518","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>Background: Mild Cognitive Impairment (MCI) is an early stage of cognitive decline and a major risk factor for dementia, typically diagnosed using neuropsychological assessments such as memory and executive function tests. While EEG-based detection has been widely explored, most approaches rely on raw signal analysis and computationally intensive deep learning models. In contrast, wearable devices use indirect behavioral proxies (e.g., activity patterns or sleep\u2013wake patterns), limiting diagnostic specificity. Although substantial clinical evidence indicates altered sleep architecture in MCI, the use of sleep stage dynamics for MCI classification remains largely unexplored. Methods: We propose a lightweight and physiologically interpretable framework using novel features based on hypnogram-derived sleep dynamics. The method was evaluated on the MASS SS1 dataset (36 healthy, 17 MCI subjects) using five classifiers\u2014Logistic Regression, Random Forest, XGBoost, Linear SVM, and RBF SVM\u2014with leave-one-subject-out validation and threshold optimization. Results: RBF SVM achieved the best performance (accuracy: 77.4%, balanced accuracy: 78.7%, sensitivity: 82.4%, specificity: 75.0%, ROC AUC: 0.778), followed by Random Forest (accuracy: 77.4%, balanced accuracy: 77.1%) and XGBoost (accuracy: 71.7%, balanced accuracy: 73.0%). Conclusions: This proof-of-concept study demonstrates that features extracted from sleep stage dynamics are effective, non-invasive, and interpretable biomarkers for early MCI detection, with strong potential for integration into wearable sleep monitoring systems.<\/jats:p>","DOI":"10.3390\/brainsci16060562","type":"journal-article","created":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T11:02:51Z","timestamp":1779793371000},"page":"562","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Wearable-Compatible Detection of Mild Cognitive Impairment Using Novel Features Based on Sleep Stage Dynamics"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9288-3931","authenticated-orcid":false,"given":"Dhanushka","family":"Wijesinghe","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND 58105, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5184-3695","authenticated-orcid":false,"suffix":"Jr.","given":"Ivan T.","family":"Lima","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND 58105, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,26]]},"reference":[{"key":"ref_1","unstructured":"Alzheimer\u2019s Association (2026, March 29). Mild Cognitive Impairment (MCI). Available online: https:\/\/www.alz.org\/alzheimers-dementia\/what-is-dementia\/related_conditions\/mild-cognitive-impairment."},{"key":"ref_2","unstructured":"Alzheimer\u2019s.gov (2026, March 29). Mild Cognitive Impairment, Available online: https:\/\/www.alzheimers.gov\/alzheimers-dementias\/mild-cognitive-impairment."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1212\/CON.0000000000000313","article-title":"Mild cognitive impairment","volume":"22","author":"Petersen","year":"2016","journal-title":"Contin. Lifelong Learn. Neurol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/0022-3956(75)90026-6","article-title":"Mini-mental state","volume":"12","author":"Folstein","year":"1975","journal-title":"J. Psychiatr. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1111\/j.1532-5415.2005.53221.x","article-title":"The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment","volume":"53","author":"Nasreddine","year":"2005","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/S1474-4422(09)70299-6","article-title":"Hypothetical model of dynamic biomarkers of the Alzheimer\u2019s pathological cascade","volume":"9","author":"Jack","year":"2010","journal-title":"Lancet Neurol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/nrneurol.2009.215","article-title":"The clinical use of structural MRI in Alzheimer disease","volume":"6","author":"Frisoni","year":"2010","journal-title":"Nat. Rev. Neurol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1016\/S1474-4422(03)00530-1","article-title":"CSF markers for incipient Alzheimer\u2019s disease","volume":"2","author":"Blennow","year":"2003","journal-title":"Lancet Neurol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1177\/13872877251413700","article-title":"Biomarkers for Alzheimer\u2019s disease and mild cognitive impairment: Recent advances in task-based EEG","volume":"110","author":"Tripanpitak","year":"2026","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chaabene, S., Hassan, B.H., Boudaya, A., Chaari, L., and Bouaziz, B. (2023). New mci detection method based on transformer and eeg data. Proceedings of the 2023 31st European Signal Processing Conference (EUSIPCO), IEEE.","DOI":"10.23919\/EUSIPCO58844.2023.10290024"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1007\/s11633-019-1197-4","article-title":"An integrated MCI detection framework based on spectral-temporal analysis","volume":"16","author":"Yin","year":"2019","journal-title":"Int. J. Autom. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Geng, D., Wang, C., Fu, Z., Zhang, Y., Yang, K., and An, H. (2022). Sleep EEG-based approach to detect mild cognitive impairment. Front. Aging Neurosci., 14.","DOI":"10.3389\/fnagi.2022.865558"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100118","DOI":"10.1016\/j.neuri.2023.100118","article-title":"A connectome-based deep learning approach for Early MCI and MCI detection using structural brain networks","volume":"3","author":"Kolahkaj","year":"2023","journal-title":"Neurosci. Inform."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1109\/TMI.2019.2928790","article-title":"Deep learning of static and dynamic brain functional networks for early MCI detection","volume":"39","author":"Kam","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"158","DOI":"10.14283\/jpad.2020.19","article-title":"Rationale for early diagnosis of mild cognitive impairment (MCI) supported by emerging digital technologies","volume":"7","author":"Sabbagh","year":"2020","journal-title":"J. Prev. Alzheimer\u2019s Dis."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"171","DOI":"10.14283\/jpad.2020.22","article-title":"Early detection of mild cognitive impairment (MCI) in an at-home setting","volume":"7","author":"Sabbagh","year":"2020","journal-title":"J. Prev. Alzheimer\u2019s Dis."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"100077","DOI":"10.1016\/j.tjpad.2025.100077","article-title":"MemScreen: A smartphone application for detection of mild cognitive impairment: A validation study: Smartphone app for MCI detection","volume":"12","author":"Dumurgier","year":"2025","journal-title":"J. Prev. Alzheimer\u2019s Dis."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Supratak, A., and Guo, Y. (2020). TinySleepNet: An efficient deep learning model for sleep stage scoring based on raw single-channel EEG. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE.","DOI":"10.1109\/EMBC44109.2020.9176741"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1109\/TNSRE.2019.2896659","article-title":"SeqSleepNet: End-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging","volume":"27","author":"Phan","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wijesinghe, D., and Lima, I.T. (2025). A lightweight neural network based on memory and transition probability for accurate real-time sleep stage classification. Brain Sci., 15.","DOI":"10.3390\/brainsci15080789"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101951","DOI":"10.1016\/j.smrv.2024.101951","article-title":"Sleep assessment using EEG-based wearables\u2013A systematic review","volume":"76","author":"Burger","year":"2024","journal-title":"Sleep Med. Rev."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liang, Y., Liu, W., and Wang, M. (2023). Characteristics of macroscopic sleep structure in patients with mild cognitive impairment: A systematic review. Front. Psychiatry, 14.","DOI":"10.3389\/fpsyt.2023.1212514"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.5665\/sleep.2956","article-title":"Polysomnographic and subjective sleep markers of mild cognitive impairment","volume":"36","author":"Atienza","year":"2013","journal-title":"Sleep"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1111\/jsr.12169","article-title":"Montreal Archive of Sleep Studies: An open-access resource for instrument benchmarking and exploratory research","volume":"23","author":"Gosselin","year":"2014","journal-title":"J. Sleep Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"409","DOI":"10.5664\/jcsm.7664","article-title":"Disconnection between self-reported and objective cognitive impairment in obstructive sleep apnea","volume":"15","author":"Gagnon","year":"2019","journal-title":"J. Clin. Sleep Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106418","DOI":"10.1016\/j.compbiomed.2022.106418","article-title":"Classification of mild cognitive impairment based on handwriting dynamics and qEEG","volume":"152","author":"Chai","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wu, R., Li, A., Xue, C., Chai, J., Qiang, Y., Zhao, J., and Wang, L. (2023). Screening for mild cognitive impairment with speech interaction based on virtual reality and wearable devices. Brain Sci., 13.","DOI":"10.3390\/brainsci13081222"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chu, K.T., Lei, W.C., Wu, M.H., Fuh, J.L., Wang, S.J., French, I.T., Chang, W.S., Chang, C.F., Huang, N.E., and Liang, W.K. (2023). A holo-spectral EEG analysis provides an early detection of cognitive decline and predicts the progression to Alzheimer\u2019s disease. Front. Aging Neurosci., 15.","DOI":"10.3389\/fnagi.2023.1195424"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s13195-023-01181-1","article-title":"Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer\u2019s disease using EEG technology","volume":"15","author":"Jiao","year":"2023","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Efron, B., and Tibshirani, R.J. (1994). An Introduction to the Bootstrap, CRC Press.","DOI":"10.1201\/9780429246593"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Varma, S., and Simon, R. (2006). Bias in error estimation when using cross-validation for model selection. BMC Bioinform., 7.","DOI":"10.1186\/1471-2105-7-91"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1162\/089976698300017197","article-title":"Approximate statistical tests for comparing supervised classification learning algorithms","volume":"10","author":"Dietterich","year":"1998","journal-title":"Neural Comput."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Davison, A.C., and Hinkley, D.V. (1997). Bootstrap Methods and Their Application, Cambridge University Press.","DOI":"10.1017\/CBO9780511802843"},{"key":"ref_36","unstructured":"Nadeau, C., and Bengio, Y. (1999). Inference for the generalization error. Adv. Neural Inf. Process. Syst., 12, Available online: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/1999\/file\/7d12b66d3df6af8d429c1a357d8b9e1a-Paper.pdf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.5665\/sleep.2802","article-title":"Sleep fragmentation and the risk of incident Alzheimer\u2019s disease and cognitive decline in older persons","volume":"36","author":"Lim","year":"2013","journal-title":"Sleep"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1016\/j.jalz.2018.12.004","article-title":"Alteration in sleep architecture and electroencephalogram as an early sign of Alzheimer\u2019s disease preceding the disease pathology and cognitive decline","volume":"15","author":"Zhang","year":"2019","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1126\/science.8235588","article-title":"Thalamocortical oscillations in the sleeping and aroused brain","volume":"262","author":"Steriade","year":"1993","journal-title":"Science"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"859","DOI":"10.3233\/JAD-2010-1290","article-title":"Global functional coupling of resting EEG rhythms is related to white-matter lesions along the cholinergic tracts in subjects with amnesic mild cognitive impairment","volume":"19","author":"Babiloni","year":"2010","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1126\/science.1241224","article-title":"Sleep drives metabolite clearance from the adult brain","volume":"342","author":"Xie","year":"2013","journal-title":"Science"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nrneurol.2013.269","article-title":"Sleep and Alzheimer disease pathology\u2014A bidirectional relationship","volume":"10","author":"Ju","year":"2014","journal-title":"Nat. Rev. Neurol."},{"key":"ref_43","unstructured":"Bishop, C.M., and Nasrabadi, N.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_44","first-page":"2079","article-title":"On over-fitting in model selection and subsequent selection bias in performance evaluation","volume":"11","author":"Cawley","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1021\/ci0342472","article-title":"The problem of overfitting","volume":"44","author":"Hawkins","year":"2004","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/1741-2560\/4\/2\/R01","article-title":"A review of classification algorithms for EEG-based brain\u2013computer interfaces","volume":"4","author":"Lotte","year":"2007","journal-title":"J. Neural Eng."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, A., Li, J., Zhang, D., Wu, W., Zhao, J., and Qiang, Y. (2023). Synergy through integration of digital cognitive tests and wearable devices for mild cognitive impairment screening. Front. Hum. Neurosci., 17.","DOI":"10.3389\/fnhum.2023.1183457"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s11571-023-10010-y","article-title":"Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals","volume":"18","author":"Said","year":"2024","journal-title":"Cogn. Neurodyn."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Po\u017ear, R., Giordani, B., and Kavcic, V. (2020). Effective differentiation of mild cognitive impairment by functional brain graph analysis and computerized testing. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0230099"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1186\/s13195-023-01368-6","article-title":"Neurophysiological markers in community-dwelling older adults with mild cognitive impairment: An EEG study","volume":"15","author":"Katayama","year":"2023","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1109\/TNSRE.2019.2911970","article-title":"A single-channel EEG-based approach to detect mild cognitive impairment via speech-evoked brain responses","volume":"27","author":"Khatun","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"120054","DOI":"10.1016\/j.neuroimage.2023.120054","article-title":"Deep learning-based EEG analysis to classify normal, mild cognitive impairment, and dementia: Algorithms and dataset","volume":"272","author":"Kim","year":"2023","journal-title":"NeuroImage"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"130","DOI":"10.2174\/1567205014666171121114930","article-title":"A speech recognition-based solution for the automatic detection of mild cognitive impairment from spontaneous speech","volume":"15","author":"Hoffmann","year":"2018","journal-title":"Curr. Alzheimer Res."}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/16\/6\/562\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T04:32:23Z","timestamp":1779942743000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/16\/6\/562"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,26]]},"references-count":53,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["brainsci16060562"],"URL":"https:\/\/doi.org\/10.3390\/brainsci16060562","relation":{},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,26]]}}}