Deep convolutional architecture-based hybrid learning for sleep arousal events detection through single-lead EEG signals

被引:5
作者
Foroughi, Andia [1 ]
Farokhi, Fardad [1 ]
Rahatabad, Fereidoun Nowshiravan [2 ]
Kashaninia, Alireza [3 ]
机构
[1] Islamic Azad Univ, Dept Biomed Engn, Cent Tehran Branch, Tehran, Iran
[2] Islamic Azad Univ, Dept Biomed Engn, Sci & Res Branch, Tehran, Iran
[3] Islamic Azad Univ, Dept Elect Engn, Cent Tehran Branch, Tehran, Iran
来源
BRAIN AND BEHAVIOR | 2023年 / 13卷 / 06期
关键词
EEG signal; deep learning; grey wolf optimization; Inception-ResNet-v2; sleep arousal; support vector machine;
D O I
10.1002/brb3.3028
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
IntroductionDetecting arousal events during sleep is a challenging, time-consuming, and costly process that requires neurology knowledge. Even though similar automated systems detect sleep stages exclusively, early detection of sleep events can assist in identifying neuropathology progression. MethodsAn efficient hybrid deep learning method to identify and evaluate arousal events is presented in this paper using only single-lead electroencephalography (EEG) signals for the first time. Using the proposed architecture, which incorporates Inception-ResNet-v2 learning transfer models and optimized support vector machine (SVM) with the radial basis function (RBF) kernel, it is possible to classify with a minimum error level of less than 8%. In addition to maintaining accuracy, the Inception module and ResNet have led to significant reductions in computational complexity for the detection of arousal events in EEG signals. Moreover, in order to improve the classification performance of the SVM, the grey wolf algorithm (GWO) has optimized its kernel parameters. ResultsThis method has been validated using pre-processed samples from the 2018 Challenge Physiobank sleep dataset. In addition to reducing computational complexity, the results of this method show that different parts of feature extraction and classification are effective at identifying sleep disorders. The proposed model detects sleep arousal events with an average accuracy of 93.82%. With the lead present in the identification, the method becomes less aggressive in recording people's EEG signals. ConclusionAccording to this study, the suggested strategy is effective in detecting arousals in sleep disorder clinical trials and may be used in sleep disorder detection clinics.
引用
收藏
页数:13
相关论文
共 39 条
  • [1] Reduction of the Dimensionality of the EEG Channels during Scoliosis Correction Surgeries Using a Wavelet Decomposition Technique
    Al-Kadi, Mahmoud I.
    Reaz, Mamun Bin Ibne
    Ali, Mohd Alauddin Mohd
    Liu, Chian Yong
    [J]. SENSORS, 2014, 14 (07): : 13046 - 13069
  • [2] Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task
    Al-Qazzaz, Noor Kamal
    Ali, Sawal Hamid Bin Mohd
    Ahmad, Siti Anom
    Islam, Mohd Shabiul
    Escudero, Javier
    [J]. SENSORS, 2015, 15 (11) : 29015 - 29035
  • [3] Altevogt B. M., 2006, SLEEP DISORDERS SLEE, DOI [DOI 10.17226/11617, 10.17226/11617]
  • [4] Identification of Electroencephalographic Arousals in Multichannel Sleep Recordings
    Alvarez-Estevez, D.
    Moret-Bonillo, V.
    [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2011, 58 (01) : 54 - 63
  • [5] [Anonymous], 2018, 2018 COMPUTING CARDI
  • [6] Badiei A., 2023, COMPUT INTEL NEUROSC, V2023, P1
  • [7] Comparison of a sleep quality index between normal and obstructive sleep apnea patients
    Balakrishnan, Ganesh
    Burli, Divya
    Burk, John R.
    Lucas, Edgar A.
    Behbehani, Khosrow
    [J]. 2005 27TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7, 2005, : 1154 - 1157
  • [8] AASM Scoring Manual Updates for 2017 (Version 2.4)
    Berry, Richard B.
    Brooks, Rita
    Gamaldo, Charlene
    Harding, Susan M.
    Lloyd, Robin M.
    Quan, Stuart F.
    Troester, Matthew T.
    Vaughn, Bradley V.
    [J]. JOURNAL OF CLINICAL SLEEP MEDICINE, 2017, 13 (05): : 665 - 666
  • [9] Bonnet MH, 2007, J CLIN SLEEP MED, V3, P133
  • [10] Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning
    Chien, Ying-Ren
    Wu, Cheng-Hsuan
    Tsao, Hen-Wai
    [J]. SENSORS, 2021, 21 (18)