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dc.contributor.authorHaider, Usman
dc.contributor.authorHanif, Muhammad
dc.contributor.authorRashid, Ahmer
dc.contributor.authorMian Qaisar, Saeed
dc.contributor.authorSubasi, Abdulhamit
dc.date.accessioned2024-01-18T05:43:30Z
dc.date.available2024-01-18T05:43:30Z
dc.date.issued2024-04-12
dc.identifier.doihttps://doi.org/10.1016/j.bspc.2023.105856en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/1351
dc.descriptionThanks to the Effat University for supporting this work under the grant number UC#9/12June2023/7.1-21(4)13.en_US
dc.description.abstractMobile healthcare is an appealing approach based on the Internet of Medical Things (IoMT) and cloud computing. It can lead to unobstructed, economical, and patient-centric healthcare solutions. The key performance indicators of such systems are dimensionality reduction, computational effectiveness, low latency, and accuracy. In this context, a novel approach is devised for EEG-based schizophrenia, a severe mental disorder that adversely affects a person’s behavior and classification. A multichannel EEG recording with suitable granularity is required for precise analysis. It can increase exponentially the data dimensionality plus complexity and computational load. The proposed solution attains an interesting trade-off between dimensionality reduction plus computational effectiveness versus accuracy. It uses the penalized sequential dictionary learning (PSDL) that incorporates channel selection. First, PSDL learns a dictionary from the input data and evaluates its performance on all EEG channels. Based on this evaluation, a subset of six channels is selected for further training in the dictionary. The proposed PSDL algorithm then incorporates a penalty term that enhances the power of the learned dictionary on the selected channels. We evaluate the proposed approach on the multi-channel EEG dataset from the Institute of Psychiatry and Neurology in Warsaw, Poland. A performance comparison is also made with counterparts. The models’ performance depends on the EEG signals’ complexity. Therefore, we tried to make our models robust and straightforward, achieving appropriate performance with minimal computational cost. The proposed method reduces the dimension in two steps. First, the count of channels is reduced to 68.42%. In the second step, the kept information, 31.58% of channels, is further reduced to 83.75% using dictionary learning. The proposed framework secures a remarkable data dimension reduction and a lower computational cost and latency than the counterparts while attaining the sparse representation classification accuracy of 89.12%. These findings are promising and confirm the potential of investing in incorporating the proposed method in contemporary mobile healthcare solutions.en_US
dc.description.sponsorshipEffat Universityen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectSchizophreniaen_US
dc.subjectElectroencephelogramen_US
dc.subjectClassificationen_US
dc.subjectSparse representationen_US
dc.subjectSparse classificationen_US
dc.subjectPenalized sequential dictionary learningen_US
dc.subjectMobile healthcareen_US
dc.titleEEG-based schizophrenia classification using penalized sequential dictionary learning in the context of mobile healthcareen_US
dc.source.journalBiomedical Signal Processing and Controlen_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labBiometrics and Sensory Systems Laben_US
dc.subject.KSAHEALTHen_US
dc.contributor.ugstudent0en_US
dc.contributor.alumnae0en_US
dc.title.projectSecure and Smart Supply Chains: Design, Traceability, and Transparencyen_US
dc.source.indexScopusen_US
dc.source.indexWoSen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.contributor.pgstudent1en_US
dc.contributor.firstauthorHaider, Usman


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