{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:17:56Z","timestamp":1774311476933,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"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>A novel approach for multichannel epilepsy seizure classification which will help to automatically locate seizure activity present in the focal brain region was proposed. This paper suggested an Internet of Things (IoT) framework based on a smart phone by utilizing a novel feature termed multiresolution critical spectral verge (MCSV), based on frequency-derived information for epileptic seizure classification which was optimized using a flower pollination algorithm (FPA). A wireless sensor technology (WSN) was utilized to record the electroencephalography (EEG) signal of epileptic patients. Next, the EEG signal was pre-processed utilizing a multiresolution-based adaptive filtering (MRAF) method. Then, the maximal frequency point at which the power spectral density (PSD) of each EEG segment was greater than the average spectral power of the corresponding frequency band was computed. This point was further optimized to extract a point termed as critical spectral verge (CSV) to extract the exact high frequency oscillations representing the actual seizure activity present in the EEG signal. Next, a support vector machine (SVM) classifier was used for channel-wise classification of the seizure and non-seizure regions using CSV as a feature. This process of classification using the CSV feature extracted from the MRAF output is referred to as the MCSV approach. As a final step, cloud-based services were employed to analyze the EEG information from the subject\u2019s smart phone. An exhaustive analysis was undertaken to assess the performance of the MCSV approach for two datasets. The presented approach showed an improved performance with a 93.83% average sensitivity, a 97.94% average specificity, a 97.38% average accuracy with the SVM classifier, and a 95.89% average detection rate as compared with other state-of-the-art studies such as deep learning. The methods presented in the literature were unable to precisely localize the origination of the seizure activity in the brain region and reported a low seizure detection rate. This work introduced an optimized CSV feature which was effectively used for multichannel seizure classification and localization of seizure origination. The proposed MCSV approach will help diagnose epileptic behavior from multichannel EEG signals which will be extremely useful for neuro-experts to analyze seizure details from different regions of the brain.<\/jats:p>","DOI":"10.3390\/s22239302","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T08:46:41Z","timestamp":1669798001000},"page":"9302","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Novel Approach for Multichannel Epileptic Seizure Classification Based on Internet of Things Framework Using Critical Spectral Verge Feature Derived from Flower Pollination Algorithm"],"prefix":"10.3390","volume":"22","author":[{"given":"Dhanalekshmi Prasad","family":"Yedurkar","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, MIT Art Design and Technology University, Pune 412201, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shilpa P.","family":"Metkar","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunication Engineering, College of Engineering Pune, Pune 411005, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6375-4123","authenticated-orcid":false,"given":"Fadi","family":"Al-Turjman","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, Mersin 10, Turkey"},{"name":"Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6578-6919","authenticated-orcid":false,"given":"Thompson","family":"Stephan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore 560054, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9152-3242","authenticated-orcid":false,"given":"Manjur","family":"Kolhar","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Arts and Science, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chadi","family":"Altrjman","sequence":"additional","affiliation":[{"name":"Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Turkey"},{"name":"Faculty of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,29]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2022, September 28). Epilepsy, Available online: http:\/\/www.who.int\/mental_health\/neurology\/epilepsy\/en\/index.html."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1111\/j.0013-9580.2005.66104.x","article-title":"Epileptic seizures and epilepsy: Definitions proposed by the international league against epilepsy (ILAE) and the international bureau for epilepsy (IBE)","volume":"46","author":"Fisher","year":"2005","journal-title":"Epilepsia"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2688","DOI":"10.1016\/j.comnet.2010.05.003","article-title":"Wireless sensor networks for healthcare: A survey","volume":"54","author":"Alemdar","year":"2014","journal-title":"Comput. Netw."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.yebeh.2009.08.024","article-title":"Real-time detection, quantification, warning, and control of epileptic seizures: The foundations for a scientific epileptology","volume":"16","author":"Osorio","year":"2009","journal-title":"Epilepsy Behav."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"061907","DOI":"10.1103\/PhysRevE.64.061907","article-title":"Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state","volume":"64","author":"Andrzejak","year":"2001","journal-title":"Phys. Rev. E"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/S0165-0270(02)00340-0","article-title":"Analysis of EEG records in an epileptic patient using wavelet transform","volume":"123","author":"Adeli","year":"2003","journal-title":"J. Neurosci. Methods"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"11413","DOI":"10.1016\/j.eswa.2012.04.023","article-title":"Time-frequency distributions in the classification of epilepsy from EEG signals","volume":"39","author":"Musselman","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1088\/0967-3334\/25\/4\/012","article-title":"Time-frequency based newborn EEG seizure detection using low and high frequency signatures","volume":"25","author":"Hassanpour","year":"2004","journal-title":"Physiol. Meas."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2361","DOI":"10.1109\/78.469854","article-title":"An adaptive optimal-kernel time-frequency representation","volume":"43","author":"Jones","year":"1995","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.yebeh.2012.05.009","article-title":"Epileptic seizure detection with linear and nonlinear features","volume":"24","author":"Yuan","year":"2012","journal-title":"Epilepsy Behav."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1109\/TBME.2006.886855","article-title":"A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy","volume":"54","author":"Adeli","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8659","DOI":"10.1016\/j.eswa.2010.06.065","article-title":"EEG signal classification using PCA, ICA, LDA and support vector machines","volume":"37","author":"Subasi","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1016\/j.eswa.2014.08.030","article-title":"Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions","volume":"42","author":"Sharma","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/TMAG.1966.1065802","article-title":"Magnetization reversal in films with biaxial anisotropy","volume":"2","author":"Doyle","year":"1966","journal-title":"IEEE Trans. Magn."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/TBME.2007.905490","article-title":"Principle component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection","volume":"55","author":"Dastidar","year":"2008","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1111\/j.1528-1167.2012.03417.x","article-title":"Spatiotemporal neuronal correlates of seizure generation in focal epilepsy","volume":"53","author":"Bower","year":"2012","journal-title":"Epilepsia"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/JBHI.2018.2829877","article-title":"Nonconvulsive epileptic seizure detection in scalp EEG using multiway data analysis","volume":"23","author":"Aldana","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_19","unstructured":"Tanaka, T., and Saito, Y. (April, January 31). Rhythmic component extraction for multichannel EEG data analysis. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, NV, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1109\/10.900270","article-title":"Automatic differentiation of multichannel EEG signals","volume":"48","author":"Peters","year":"2001","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jbi.2014.02.005","article-title":"Automated patient-specific classification of long-term Electroencephalography","volume":"49","author":"Kiranyazet","year":"2014","journal-title":"J. Biomed. Inform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2591","DOI":"10.1109\/TBME.2018.2809798","article-title":"Epileptic seizure detection in long-term EEG recordings by using wavelet-based directed transfer function","volume":"65","author":"Dong","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103275","DOI":"10.1016\/j.ebiom.2021.103275","article-title":"Evaluation of combined artificial intelligence and neurologist assessment to annotate scalp electroencephalography data","volume":"66","author":"Roy","year":"2021","journal-title":"EBioMedicine"},{"key":"ref_24","first-page":"76007","article-title":"EEG Feature Extraction for Person Identification Using Wavelet Decomposition and Multi-Objective Flower Pollination Algorithm","volume":"6","author":"Alyasseri","year":"2018","journal-title":"IEEE Access Spec. Sect. New Trends Brain Signal Process. Anal."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.future.2013.07.009","article-title":"CoCaMAAL: A cloud-oriented context-aware middleware in ambient assisted living","volume":"35","author":"Forkan","year":"2014","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.future.2013.12.015","article-title":"BodyCloud: A SaaS approach for community body sensor networks","volume":"35","author":"Fortino","year":"2014","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.simpat.2014.06.015","article-title":"Cloudlet-based efficient data collection in wireless body area networks","volume":"50","author":"Quwaider","year":"2015","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.future.2015.01.009","article-title":"Healing on the cloud: Secure cloud architecture for medical wireless sensor networks","volume":"55","author":"Lounis","year":"2016","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101794","DOI":"10.1016\/j.bspc.2019.101794","article-title":"Multiresolution approach for artifacts removal and localization of seizure onset zone in epileptic EEG signal","volume":"57","author":"Yedurkar","year":"2020","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1109\/TBME.2004.827072","article-title":"BCI2000: A general-purpose brain-computer interface (BCI) system","volume":"51","author":"Schalk","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Harati, A., Choi, S., Tabrizi, M., Obeid, I., Picone, J., and Jacobson, M.P. (2013, January 3\u20135). The Temple University Hospital EEG Corpus. Proceedings of the IEEE Global Conference on Signal and Information Processing, Austin, TX, USA. Available online: www.nedcdata.org.","DOI":"10.1109\/GlobalSIP.2013.6736803"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/S1388-2457(99)00284-9","article-title":"Automatic spike detection via an artificial neural network using raw EEG data: Effects of data preparation and implications in the limitations of online recognition","volume":"111","author":"Ko","year":"2000","journal-title":"Clin. Neurophysiol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"052902","DOI":"10.1103\/PhysRevE.67.052902","article-title":"Wavelet analysis of epileptic spikes","volume":"67","author":"Miroslaw","year":"2003","journal-title":"Phys. Rev. E"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40708-018-0088-8","article-title":"Automated epileptic seizures detection using multi-features and multilayer perceptron neural network","volume":"5","author":"Sriraam","year":"2018","journal-title":"Brain Inform."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"R32","DOI":"10.1088\/1741-2560\/4\/2\/R03","article-title":"A survey of signal processing algorithms in brain\u2013computer interfaces based on electrical brain signals","volume":"4","author":"Bashashati","year":"2007","journal-title":"J. Neural Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TNSRE.2018.2818123","article-title":"A Novel Signal Modeling Approach for Classification of Seizure and Seizure-free EEG Signals","volume":"26","author":"Gupta","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"23","DOI":"10.3233\/THC-181548","article-title":"Automatic epileptic seizure classification in multichannel EEG time series with linear discriminant analysis","volume":"28","author":"Gao","year":"2020","journal-title":"Technol. Health Care"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.compbiomed.2017.09.017","article-title":"Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals","volume":"100","author":"Acharya","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_40","first-page":"16","article-title":"Deep residual learning for automatic seizure detection","volume":"21","author":"Golmohammadi","year":"2018","journal-title":"Seizure Hrs."},{"key":"ref_41","first-page":"3592","article-title":"A novel spike detection algorithm based on multi-channel of bect eeg signals","volume":"67","author":"Wang","year":"2020","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"key":"ref_42","first-page":"1","article-title":"Analysis of epileptic EEG signals by using dynamic mode decomposition and spectrum","volume":"41","author":"Cura","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2215","DOI":"10.1016\/j.bspc.2020.102215","article-title":"A robust deep learning approach for automatic classification of seizures against non-seizures","volume":"64","author":"Yao","year":"2021","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/JBHI.2017.2654479","article-title":"Epileptic seizure classification of EEGs using time\u2013frequency analysis based multiscale radial basis functions","volume":"22","author":"Li","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.cmpb.2014.04.001","article-title":"Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm","volume":"115","author":"Zhu","year":"2014","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"101707","DOI":"10.1016\/j.bspc.2019.101707","article-title":"A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques","volume":"56","author":"Amin","year":"2020","journal-title":"Biomed. Signal Process. Control."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9302\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:29:24Z","timestamp":1760146164000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9302"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,29]]},"references-count":46,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22239302"],"URL":"https:\/\/doi.org\/10.3390\/s22239302","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,29]]}}}