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Critical Reviews™ in Biomedical Engineering

Publicado 6 números por año

ISSN Imprimir: 0278-940X

ISSN En Línea: 1943-619X

SJR: 0.262 SNIP: 0.372 CiteScore™:: 2.2 H-Index: 56

Indexed in

A Review of Sleep Disorder Diagnosis by Electromyogram Signal Analysis

Volumen 43, Edición 1, 2015, pp. 1-20
DOI: 10.1615/CritRevBiomedEng.2015012037
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SINOPSIS

Sleep and sleep-related problems play a role in a large number of human disorders and affect every field of medicine. It is estimated that 50 to 70 million Americans suffer from a chronic sleep disorder, which hinders their daily life, affects their health, and confers a significant economic burden to society. The negative public health consequences of sleep disorders are enormous and could have long-term effects, including increased risk of hypertension, diabetes, obesity, heart attack, stroke and in some cases death. Polysomnographic modalities can monitor sleep cycles to identify disrupted sleep patterns, adjust the treatments, increase therapeutic options and enhance the quality of life of recording the electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG). Although the skills acquired by medical facilitators are quite extensive, it is just as important for them to have access to an assortment of technologies and to further improve their monitoring and treatment capabilities. Computer-aided analysis is one advantageous technique that could provide quantitative indices for sleep disorder screening. Evolving evidence suggests that Parkinson's disease may be associated with rapid eye movement sleep behavior disorder (RBD). With this article, we are reviewing studies that are related to EMG signal analysis for detection of neuromuscular diseases that result from sleep movement disorders. As well, the article describes the recent progress in analysis of EMG signals using temporal analysis, frequency-domain analysis, time-frequency, and sparse representations, followed by the comparison of the recent research.

CITADO POR
  1. Shokrollahi Mehrnaz, Krishnan Sridhar, Dopsa Dustin D., Muir Ryan T., Black Sandra E., Swartz Richard H., Murray Brian J., Boulos Mark I., Nonnegative matrix factorization and sparse representation for the automated detection of periodic limb movements in sleep, Medical & Biological Engineering & Computing, 54, 11, 2016. Crossref

  2. Boostani Reza, Karimzadeh Foroozan, Nami Mohammad, A comparative review on sleep stage classification methods in patients and healthy individuals, Computer Methods and Programs in Biomedicine, 140, 2017. Crossref

  3. Shokrollahi Mehrnaz, Rudzicz Frank, Vena Daniel, Bradley T. Douglas, Yadollahi Azadeh, A novel approach for acoustic estimation of neck fluid volume between men and women, Medical & Biological Engineering & Computing, 56, 1, 2018. Crossref

  4. He Bin, Zhang Lin, Zhuang Jian-hua, Xu Jin, Li Peng, Peng Hua, The effects of different meditation exercises on sleep quality in older people: a network meta-analysis, European Geriatric Medicine, 10, 4, 2019. Crossref

  5. Jarchi Delaram, Andreu-Perez Javier, Kiani Mehrin, Vysata Oldrich, Kuchynka  Jiri, Prochazka Ales, Sanei Saeid, Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning, Sensors, 20, 9, 2020. Crossref

  6. Modarres Mo H., Elliott Jonathan E., Weymann Kristianna B., Pleshakov Dennis, Bliwise Donald L., Lim Miranda M., Validation of Visually Identified Muscle Potentials during Human Sleep Using High Frequency/Low Frequency Spectral Power Ratios, Sensors, 22, 1, 2021. Crossref

  7. Mathew Ammu Anna, S. Vivekanandan, A Review on Sleep Disorder Analysis and Applications Based on Artificial Intelligence, in Advancing the Investigation and Treatment of Sleep Disorders Using AI, 2021. Crossref

  8. Liu Yufei, Niu Long, Liu Xinyao, Cheng Cheng, Le Weidong, Recent Progress in Non-motor Features of Parkinson’s Disease with a Focus on Circadian Rhythm Dysregulation, Neuroscience Bulletin, 37, 7, 2021. Crossref

  9. Elgart Michael, Redline Susan, Sofer Tamar, Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research, Neurotherapeutics, 18, 1, 2021. Crossref

  10. Kazemi Alireza, McKeown Martin J., Mirian Maryam S., Sleep staging using semi-unsupervised clustering of EEG: Application to REM sleep behavior disorder, Biomedical Signal Processing and Control, 75, 2022. Crossref

  11. Cheon Jaehwan, Kim Mikyung, Comprehensive effects of various nutrients on sleep, Sleep and Biological Rhythms, 20, 4, 2022. Crossref

  12. Sharma Manish, Darji Jay, Thakrar Madhav, Acharya U. Rajendra, Automated identification of sleep disorders using wavelet-based features extracted from electrooculogram and electromyogram signals, Computers in Biology and Medicine, 143, 2022. Crossref

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