Sleep disorders have negative effects on human health. Sleep Disorder Breathing (SDB) and Periodic Leg Movement (PLM) are common sleep disorders that happen during sleep. Early detection of SDB and PLM from Non-Rapid Eye Movement (NREM) can protect patients from hypertension and cardiovascular diseases. In this study, we propose a novel deep learning architecture DeepSDBPLM for classifying Normal, SDB and PLM from NREM using Electroencephalogram (EEG) and Electromyogram (EMG) signals. Our proposed model is tested in three different classification problems using ISRUC-Sleep database. The results show that our proposed model achieves the best result of F1 score as compared to the state-of-the-art techniques.


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    Titel :

    Detecting Sleep Disorders from NREM Using DeepSDBPLM


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Ahmad, Nur Syazreen (Herausgeber:in) / Mohamad-Saleh, Junita (Herausgeber:in) / Teh, Jiashen (Herausgeber:in) / Almutairi, Haifa (Autor:in) / Hassan, Ghulam Mubashar (Autor:in) / Datta, Amitava (Autor:in)

    Kongress:

    International Conference on Robotics, Vision, Signal Processing and Power Applications ; 2021 April 05, 2021 - April 06, 2021



    Erscheinungsdatum :

    31.03.2024


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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