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dc.contributor.authorSibghatullah I Khan
dc.contributor.authorMian Qaisar, Saeed
dc.contributor.authorAlberto López Martínez
dc.contributor.authorHumaira Nisar
dc.contributor.authorFrancisco Ferrero Martín
dc.date.accessioned2023-08-17T10:37:51Z
dc.date.available2023-08-17T10:37:51Z
dc.date.issued2023-07-13
dc.identifier.doi10.1109/I2MTC53148.2023.10176074en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/1003
dc.description.abstractSchizophrenia is a mental illness that can negatively impact a patient's mental abilities, emotional propensities, and the standard of their private and social lives. Processing EEG data has evolved into a useful tool for tracking and identifying psychological brain states. In this framework, this paper focus on developing an automated approach for recognizing schizophrenia using non-invasive EEG signals. The EEG signals are segmented and onward decomposed by using the Variational Mode Decomposition (VMD). Each mode is termed a variational mode function (VMF). Onward, features from each intended VMF are mined based on a Rose Spiral Curve (RSC). The mined features are concatenated to present an instance. Afterward, the most pertinent features are selected using the Butterfly Optimization Algorithm (BOA). The selected feature set is conveyed to the classification module. Two classification approaches are applied in this study namely, the k-nearest neighbor (k-NN) and Random Forest (RF). The applicability is tested by using a publicly available EEG schizophrenia dataset. The highest accuracy of 89.0 % is secured for the case of RF.en_US
dc.publisherIEEEen_US
dc.subjectElectroencephalogramen_US
dc.subjectSegmentationen_US
dc.subjectVariational Mode Decompositionen_US
dc.subjectMetaheuristic Optimizationen_US
dc.titleEEG Signal based Schizophrenia Recognition by using VMD Rose Spiral Curve Butterfly Optimization and Machine Learningen_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labNAen_US
dc.subject.KSAICTen_US
dc.contributor.ugstudentNAen_US
dc.contributor.alumnaeNAen_US
dc.source.indexScopusen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.contributor.pgstudentNAen_US
dc.contributor.firstauthorSibghatullah I Khan
dc.conference.locationKuala Lumpur, Malaysiaen_US
dc.conference.name2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)en_US
dc.conference.date2023-05-22


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