Deep rhythm and long short term memory-based drowsiness detection

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dc.authorid0000-0002-2917-3736en_US
dc.contributor.authorTürkoğlu, Muammer
dc.contributor.authorAlçin, Ömer Faruk
dc.contributor.authorAslan, Muzaffer
dc.contributor.authorAl-Zebari, Adel
dc.contributor.authorŞengur, Abdülkadir
dc.date.accessioned2021-09-27T11:55:08Z
dc.date.available2021-09-27T11:55:08Z
dc.date.issued2021en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn this paper, a deep-rhythm-based approach is proposed for the efficient detection of drowsiness based on EEG recordings. In the proposed approach, EEG images are used instead of signals where the time and frequency information of the EEG signals are incorporated. The EEG signals are converted to EEG images using the time-frequency transformation method. The Short-Time-Fourier-Transform (STFT) is used for this transformation due to its simplicity. The rhythm images are then extracted by dividing the EEG images based on frequency intervals. EEG signals contain five rhythms, namely Delta rhythm (0–4?Hz), Theta rhythm (4–8?Hz), Alpha rhythm (8–12?Hz), Beta rhythm (12–30?Hz), and Gamma rhythm (30–50?Hz). From each rhythm image, deep features are extracted based on a pre-trained convolutional neural network (CNN) model, with pre-trained residual network (ResNet) models such as ResNet18, ResNet50, and ResNet101. The obtained deep features from each rhythm image are fed into the Long-Short-Term-Memory (LSTM) layer, and the LSTM layers are then sequentially connected to each other. After the last LSTM layer, a fully-connected layer, a softmax layer, and a classification layer are employed in order to detect the class labels of the input EEG signals. Various experiments were conducted with the MIT/BIH Polysomnographic Dataset. The experiments showed that the concatenated ResNet features achieved an accuracy score of 97.92%. The obtained accuracy score was also compared with the state-of-the-art scores and, to the best of our knowledge, the proposed method achieved the best accuracy score among the methods compared.en_US
dc.identifier.citationTurkoglu, M., Alcin, O. F., Aslan, M., Al-Zebari, A., & Sengur, A. (2021). Deep rhythm and long short term memory-based drowsiness detection. Biomedical Signal Processing and Control, 65, 102364.en_US
dc.identifier.doi10.1016/j.bspc.2020.102364
dc.identifier.endpage7en_US
dc.identifier.issn1746-8094en_US
dc.identifier.issn1746-8108en_US
dc.identifier.scopus2-s2.0-85097132546en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2020.102364
dc.identifier.urihttps://hdl.handle.net/20.500.12899/442
dc.identifier.volume65en_US
dc.identifier.wosWOS:000614122200027en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAlçin, Ömer Faruk
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofBiomedical Signal Processing and Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectEEG imagesen_US
dc.subjectDrowsinessen_US
dc.subjectDeep featuresen_US
dc.subjectResidual networksen_US
dc.subjectLSTMen_US
dc.titleDeep rhythm and long short term memory-based drowsiness detectionen_US
dc.typeArticleen_US

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