Automated detection of alzheimer’s disease using raw EEG time series via. DWT-CNN model

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dc.authorid0000-0001-9245-6790en_US
dc.authorid0000-0002-9368-8902en_US
dc.contributor.authorŞeker, Mesut
dc.contributor.authorÖzerdem, Mehmet Siraç
dc.date.accessioned2023-03-02T05:42:35Z
dc.date.available2023-03-02T05:42:35Z
dc.date.issued2022en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractDementia is an age-related neurological disease and gives rise to profound cognitive decline in patients’ life. Alzheimer’s Disease (AD) is the progression of dementia and AD patients generally have memory loss and behavioral disorders. It is possible to determine the stage of dementia by developing automated systems via. signals obtained from patients. EEG is a popular brain monitoring system due to its cost effective, non-invasive implementation, and higher time resolution. In current study, we include participants of 24 HC (12 eyes open (EO), 12 eyes closed (EC)), and 24 AD (HC (12 eyes open (EO), 12 eyes closed (EC)). The aim of current study is to design a practical AD detection tool for AD/HC participants with a model called DWT-CNN. We performed Discrete Wavelet Transform (DWT) to extract EEG sub-bands. A Conv2D architecture is applied to raw samples of related EEG sub-bands. According to obtained performance metrics calculated from confusion matrices, all AD and HC time series are correctly classified for alpha band and full band range under both EO and EC. Classification rate of AD vs. HC increases under EO state in all cases even if EC is commonly preferred in other studies. We will add MCI patients with equal size and similar demographics and repeat the experimental steps to develop early alert system in future studies. Adding more participants will also increase generalization ability of method. It is also promising study to combine EEG with different modalities (2D TF image conversion, or MRI) in a multimodal approach.en_US
dc.identifier.citationŞeker, M. ve Özerdem, M. S. (2022). Automated detection of alzheimer’s disease using raw EEG time series via. DWT-CNN model. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 13(4), 673-684.en_US
dc.identifier.doi10.24012/dumf.1197722
dc.identifier.endpage684en_US
dc.identifier.issn1309-8640
dc.identifier.issn2146-4391
dc.identifier.issue4en_US
dc.identifier.startpage673en_US
dc.identifier.urihttps://dergipark.org.tr/tr/download/article-file/2743819
dc.identifier.urihttps://hdl.handle.net/11468/11297
dc.identifier.volume13en_US
dc.institutionauthorŞeker, Mesut
dc.institutionauthorÖzerdem, Mehmet Siraç
dc.language.isoenen_US
dc.publisherDicle Üniversitesi Mühendislik Fakültesien_US
dc.relation.ispartofDicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep learningen_US
dc.subjectAlzheimer’s diseaseen_US
dc.subjectCNNen_US
dc.subjectEEGen_US
dc.subjectDisease detectionen_US
dc.titleAutomated detection of alzheimer’s disease using raw EEG time series via. DWT-CNN modelen_US
dc.titleAutomated detection of alzheimer’s disease using raw EEG time series via. DWT-CNN model
dc.typeArticleen_US

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