{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T16:41:37Z","timestamp":1791909697958,"version":"4.3.4"},"reference-count":53,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,7]],"date-time":"2025-12-07T00:00:00Z","timestamp":1765065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate and robust classification of dementia subtypes using non-invasive electroencephalography (EEG) signals remains a critical challenge for clinicians and researchers in the field of neuroscience. Traditional methods often rely on limited spectral features, overlooking the rich structural and geometric information inherent in EEG dynamics. CONECT (Complex Network Conversion and Topology), a novel framework, is introduced and built upon four core innovations. First, EEG time series are transformed into weighted networks using a novel Angle-Relation Connection (ARC) rule, a geometry-based approach that links time points based on angular monotonicity. Secondly, a tunable edge-weighting function is introduced by integrating amplitude, temporal, and angular components, providing adaptable heuristics adaptable to the most promising biomarker, i.e., curvature-driven features in dementia. Additionally, two new graph-based EEG features, the Weighted Angular Irregularity Index (WAII) and the Curvature-Based Edge Feature Index (CBEFI), are proposed as potential biomarkers to capture localized irregularity and signal geometry, respectively. For the first time in a dementia EEG classification study using the OpenNeuro ds004504 dataset (raw), Ant Colony Optimization (ACO) is applied as a feature selection technique to select the most discriminative features and improve model classification and transparency. The classification results demonstrate CONECT\u2019s potential as a promising, interpretable, and geometry-informed framework for accurate and practical dementia subtype diagnosis.<\/jats:p>","DOI":"10.3390\/s25247439","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T15:30:07Z","timestamp":1765207807000},"page":"7439","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["CONECT: Novel Weighted Networks Framework Leveraging Angle-Relation Connection (ARC) and Metaheuristic Algorithms for EEG-Based Dementia Classification"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1803-8196","authenticated-orcid":false,"given":"Akashdeep","family":"Singh","sequence":"first","affiliation":[{"name":"Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC 3011, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3124-1187","authenticated-orcid":false,"given":"Supriya","family":"Supriya","sequence":"additional","affiliation":[{"name":"Centre for Artificial Intelligence Research and Optimisation (AIRO), Torrens University, Melbourne, VIC 3000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2491-0546","authenticated-orcid":false,"given":"Siuly","family":"Siuly","sequence":"additional","affiliation":[{"name":"Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC 3011, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8465-0996","authenticated-orcid":false,"given":"Hua","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC 3011, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mouazen, B., Bendaouia, A., Bellakhdar, O., Laghdaf, K., Ennair, A., Abdelwahed, E.H., and De Marco, G. (2025). Transparent EEG Analysis: Leveraging Autoencoders, Bi-LSTMs, and SHAP for Improved Neurodegenerative Diseases Detection. Sensors, 25.","DOI":"10.20944\/preprints202507.0639.v1"},{"key":"ref_2","unstructured":"WHO (2025, October 15). Dementia. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/dementia."},{"key":"ref_3","unstructured":"Alzheimer\u2019s Disease International (2021). World Alzheimer Report 2021: Journey Through the Diagnosis of Dementia, Alzheimer\u2019s Disease International. Available online: https:\/\/www.alzint.org\/u\/World-Alzheimer-Report-2021.pdf."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gillani, N., and Arslan, T. (2021). Intelligent Sensing Technologies for the Diagnosis, Monitoring and Therapy of Alzheimer\u2019s Disease: A Systematic Review. Sensors, 21.","DOI":"10.3390\/s21124249"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"130","DOI":"10.3109\/09540261.2013.776523","article-title":"The Epidemiology of Frontotemporal Dementia","volume":"25","author":"Onyike","year":"2013","journal-title":"Int. Rev. Psychiatry"},{"key":"ref_6","first-page":"343","article-title":"Differentiating Alzheimer\u2019s Disease from Frontotemporal Dementia: A Meta-Review of Diagnostic Accuracy","volume":"35","author":"Varma","year":"2017","journal-title":"Neurol. Clin."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, H., Song, J., and Gong, J. (2024). Exploring Task-Related EEG for Cross-Subject Early Alzheimer\u2019s Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis. Sensors, 25.","DOI":"10.3390\/s25010052"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s40708-025-00260-3","article-title":"Advancing Alzheimer\u2019s Disease Detection: A Novel Convolutional Neural Network-Based Framework Leveraging EEG Data and Segment Length Analysis","volume":"12","author":"Tawhid","year":"2025","journal-title":"Brain Inform."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Neto, E., Allen, E.A., Aurlien, H., Nordby, H., and Eichele, T. (2015). EEG Spectral Features Discriminate between Alzheimer\u2019s and Vascular Dementia. Front. Neurol., 6.","DOI":"10.3389\/fneur.2015.00025"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Falaschetti, L., Biagetti, G., Alessandrini, M., Turchetti, C., Luzzi, S., and Crippa, P. (2024). Multi-Class Detection of Neurodegenerative Diseases from EEG Signals Using Lightweight LSTM Neural Networks. Sensors, 24.","DOI":"10.3390\/s24206721"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1186\/s13195-023-01280-z","article-title":"Beta to Theta Power Ratio in EEG Periodic Components as a Potential Biomarker in Mild Cognitive Impairment and Alzheimer\u2019s Dementia","volume":"15","author":"Azami","year":"2023","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xu, Q., An, L., Yang, H., and Hong, K.-S. (2025). A Multi-Graph Convolutional Network Method for Alzheimer\u2019s Disease Diagnosis Based on Multi-Frequency EEG Data with Dual-Mode Connectivity. Front. Neurosci., 19.","DOI":"10.3389\/fnins.2025.1555657"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pineda, A.M., Ramos, F.M., Betting, L.E., and Campanharo, A.S.L.O. (2020). Quantile Graphs for EEG-Based Diagnosis of Alzheimer\u2019s Disease. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0231169"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"871","DOI":"10.3233\/JAD-210394","article-title":"Contribution of Graph Theory Applied to EEG Data Analysis for Alzheimer\u2019s Disease versus Vascular Dementia Diagnosis","volume":"82","author":"Vecchio","year":"2021","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1007\/s12559-025-10447-9","article-title":"Investigating Brain Lobe Biomarkers to Enhance Dementia Detection Using EEG Data","volume":"17","author":"Siuly","year":"2025","journal-title":"Cogn. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1038\/nrn3801","article-title":"Modern Network Science of Neurological Disorders","volume":"15","author":"Stam","year":"2014","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_17","first-page":"282","article-title":"Epilepsy Detection from Weighted EEG Graph (WEG) Using Novel Feature-Normalized Weighted Forgotten Topological Index (NWFT-Index)","volume":"Volume 15336","author":"Siuly","year":"2025","journal-title":"Health Information Science (HIS 2024)"},{"key":"ref_18","first-page":"3-1","article-title":"Weighted Complex Network-Based Framework for Epilepsy Detection from EEG Signals","volume":"Volume 1","author":"Bajaj","year":"2020","journal-title":"Modelling and Analysis of Active Biopotential Signals in Healthcare"},{"key":"ref_19","unstructured":"Fraga, A., Falk, R., and B\u00f3veda, J.A. (2020). Graph Theory in EEG-Based Neurodegenerative Diagnosis: A Critical Review. Front. Neurosci., 14."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"afae121","DOI":"10.1093\/ageing\/afae121","article-title":"Small World Derived Index to Distinguish Alzheimer\u2019s Type Dementia and Healthy Subjects","volume":"53","author":"Vecchio","year":"2024","journal-title":"Age Ageing"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Frantzidis, C.A., Vivas, A.B., Tsolaki, A., Klados, M.A., Tsolaki, M., and Bamidis, P.D. (2014). Functional Disorganization of Small-World Brain Networks in Mild Alzheimer\u2019s Disease and Amnestic Mild Cognitive Impairment: An EEG Study Using Relative Wavelet Entropy (RWE). Front. Aging Neurosci., 6.","DOI":"10.3389\/fnagi.2014.00224"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4972","DOI":"10.1073\/pnas.0709247105","article-title":"From time series to complex networks: The visibility graph","volume":"105","author":"Lacasa","year":"2008","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1007\/s00702-010-0450-3","article-title":"New diagnostic EEG markers of the Alzheimer\u2019s disease using visibility graph","volume":"117","author":"Ahmadlou","year":"2010","journal-title":"J. Neural Transm."},{"key":"ref_24","unstructured":"Wu, G., Jiang, H., Wang, X., and Zhou, Y. (2024). Global Efficiency and Path Length in Alzheimer\u2019s Disease. Front. Aging Neurosci., 16."},{"key":"ref_25","first-page":"145","article-title":"EEG network analysis in Alzheimer\u2019s disease: Connectivity, graph theory, and machine learning","volume":"14","author":"Bai","year":"2021","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_26","first-page":"123","article-title":"A Novel EEG Network Approach for Early Detection of Alzheimer\u2019s Disease","volume":"30","author":"Ahmadlou","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Nigro, S., Filardi, M., Tafuri, B., De Blasi, R., Cedola, A., Gigli, G., and Logroscino, G. (2022). The Role of Graph Theory in Evaluating Brain Network Alterations in Frontotemporal Dementia. Front. Neurol., 13.","DOI":"10.3389\/fneur.2022.910054"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s10548-018-0674-3","article-title":"Cortical Network Topology in Prodromal and Mild Dementia Due to Alzheimer\u2019s Disease: Graph Theory Applied to Resting State EEG","volume":"32","author":"Franciotti","year":"2019","journal-title":"Brain Topogr."},{"key":"ref_29","unstructured":"OpenNeuro (2025). EEG Dataset for Dementia Patients (Version 1.0.8, ds004504), OpenNeuro. Available online: https:\/\/openneuro.org\/datasets\/ds004504\/versions\/1.0.8."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Miltiadous, A., Tzimourta, K.D., Afrantou, T., Ioannidis, P., Grigoriadis, N., Tsalikakis, D.G., Angelidis, P., Tsipouras, M.G., Glavas, E., and Giannakeas, N. (2024). A Dataset of EEG Recordings from Alzheimer\u2019s Disease, Frontotemporal Dementia, and Healthy Subjects. OpenNeuro, Dataset.","DOI":"10.3390\/data8060095"},{"key":"ref_31","unstructured":"Supriya, S., Jan, T., Sidnal, N., and Thompson-Whiteside, S.T. (2022). Alcoholic EEG Data Classification Using Weighted Graph-Based Technique. Health Information Science: 11th International Conference, HIS 2022, Proceedings of HIS 2022, Cham, Switzerland, 28\u201330 October 2022, Springer Nature Switzerland."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Vicchietti, M.L., Ramos, F.M., Betting, L.E., Rocco, P., Fontes, M., Rittner, L., Doretto, V., Lobosco, M., and Cordeiro, F. (2023). Computational methods of EEG signals analysis for Alzheimer\u2019s disease classification. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-32664-8"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yoder, K.J., Brookshire, G., Glatt, R.M., Merrill, D.A., Gerrol, S., Quirk, C., and Lucero, C. (2024). Fractal Dimension Distributions of Resting-State Electroencephalography (EEG) Improve Detection of Dementia and Alzheimer\u2019s Disease Compared to Traditional Fractal Analysis. Clin. Transl. Neurosci., 8.","DOI":"10.1101\/2024.06.10.24308727"},{"key":"ref_34","unstructured":"Hirsch, C., Jahnel, B., and Juh\u00e1sz, P. (2025). Functional Limit Theorems for Edge Counts in Dynamic Random Connection Hypergraphs. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hu, S., Gong, X., Huang, X., Ruan, J., and Valdes-Sosa, P.A. (2025). Exploring the Distribution of Connectivity Weights in Resting-State EEG Networks. arXiv.","DOI":"10.1016\/j.ijpsycho.2025.112719"},{"key":"ref_36","first-page":"126389","article-title":"Gini Index as an EEG Feature","volume":"10","author":"Chen","year":"2022","journal-title":"IEEE Access"},{"key":"ref_37","first-page":"195","article-title":"Epilepsy Detection from EEG Using Complex Network Techniques: A Review","volume":"14","author":"Supriya","year":"2021","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Dorigo, M., and St\u00fctzle, T. (2004). Ant Colony Optimization, MIT Press.","DOI":"10.7551\/mitpress\/1290.001.0001"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.neucom.2014.03.053","article-title":"Enriched Ant Colony Optimization and Its Application in Feature Selection","volume":"142","author":"Forsati","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Xue, X.-D., Cheng, X.-D., Xu, B., Wang, H.-L., and Jiang, C.-P. (2010, January 29\u201330). The Basic Principle and Application of Ant Colony Optimization Algorithm. Proceedings of the 2010 International Conference on Artificial Intelligence and Education (ICAIE), Hangzhou, China.","DOI":"10.1109\/ICAIE.2010.5641500"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"6554","DOI":"10.1109\/ACCESS.2016.2612242","article-title":"Weighted Visibility Graph with Complex Network Features in the Detection of Epilepsy","volume":"4","author":"Supriya","year":"2016","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhan, Y., Zhou, Y., Bai, G., and Ge, Y. (2024). Bagging Improves the Performance of Deep Learning-Based Semantic Segmentation with Limited Labeled Images: A Case Study of Crop Segmentation for High-Throughput Plant Phenotyping. Sensors, 24.","DOI":"10.3390\/s24113420"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, H., He, Z., Chen, H., and Zhang, Y. (2018). A Video Based Fire Smoke Detection Using Robust AdaBoost. Sensors, 18.","DOI":"10.3390\/s18113780"},{"key":"ref_45","first-page":"224","article-title":"EEG Sleep Stages Analysis and Classification Based on Weighed Complex Network Features","volume":"2","author":"Supriya","year":"2018","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s13755-020-00129-1","article-title":"Automated Epilepsy Detection Techniques using EEG Data: A Topical Review","volume":"8","author":"Supriya","year":"2020","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_47","unstructured":"Siuly, S., Xing, C., Li, X., and Zhou, R. (2025). Myocardial Band Transformer Network for Detecting Myocardial Ischemia in 2D Echocardiography. Health Information Science. HIS 2024, Springer. Lecture Notes in Computer Science."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.3390\/rs4092661","article-title":"Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data","volume":"4","author":"Immitzer","year":"2012","journal-title":"Remote Sens."},{"key":"ref_49","unstructured":"Hasan, M.M. (2024). Towards Diagnosis of Dementia: Microstate Analysis of EEG Signals and Classification Using Machine Learning. [Master\u2019s Thesis, Oslo Metropolitan University]."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ma, Y., Bland, J.K.S., Yoshikawa, G., and Fujinami, T. (2024). Classification of Alzheimer\u2019s Disease and Frontotemporal Dementia Using Electroencephalography to Quantify Communication between Electrode Pairs. Diagnostics, 14.","DOI":"10.3390\/diagnostics14192189"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zheng, H., Xiong, X., and Zhang, X. (2024). Multi-Threshold Recurrence Rate Plot: A Novel Methodology for EEG Analysis in Alzheimer\u2019s Disease and Frontotemporal Dementia. Brain Sci., 14.","DOI":"10.3390\/brainsci14060565"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, J., Liu, F., and Wang, Z. (2023). Multi-Feature Fusion Learning for Alzheimer\u2019s Disease Prediction Using EEG Signals in Resting State. Front. Neurosci., 17.","DOI":"10.3389\/fnins.2023.1272834"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Lal, U., Chikkankod, A.V., and Longo, L. (2024). A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer\u2019s Disease with Electroencephalography in Resting-State Adults. Brain Sci., 14.","DOI":"10.3390\/brainsci14040335"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/24\/7439\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T15:32:39Z","timestamp":1765207959000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/24\/7439"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,7]]},"references-count":53,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["s25247439"],"URL":"https:\/\/doi.org\/10.3390\/s25247439","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,7]]}}}