A Review on Automatic Detection Methods of Sleep Spindles

Authors

  • Shinanang Promkaew Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy
  • Pornwanut Kitudom Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy
  • Isaree Suwansombat Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy
  • Thamonwan Thongbun Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy
  • Kaewklao Thavornwattana Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy
  • Santitham Prom-on Faculty of Engineering, King Mongkut’s University of Technology Thonburi
  • Woranich Hinthong Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy

Keywords:

Electroencephalography (EEG), Signal processing, Machine learning, Sleep spindle, Automatic detection

Abstract

Sleep spindle is a pattern of brainwave during non-rapid eye movement (NREM) in the second stage of sleep. There are several indications that Sleep spindles might play an important role in memory consolidation, and neurodegenerative disorders such as Alzheimer’s disease andinsomnia. Sleep spindle is commonly annotated by visual inspection of experts which is time consuming, and the task has risk of high error due to over-reliance on the experts’ skill or variations in characteristics of Sleep spindle. Modern research aims to study machine learning to develop the efficient automatic Sleep spindle detectors, emulate human annotations, and solve the stated problems. However, there are few review articles on the subject. This article summarizes and compares the automatic detection methods of Sleep spindle. The workflow can be summarized into five steps: data collection, data preprocessing, feature extraction, modeling, and model evaluation. This article reveals the variation of Machine Learning application and study on supervised model can detect Sleep spindle equivalent to experts. Nevertheless, the model need customization to suit data diversity. In addition, the discussion includes recommendation and possibility for further studies.

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Author Biographies

Shinanang Promkaew, Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy

 

 

Pornwanut Kitudom, Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy

 

 

Isaree Suwansombat, Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy

 

 

Thamonwan Thongbun, Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy

 

 

Kaewklao Thavornwattana, Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy

 

 

Santitham Prom-on, Faculty of Engineering, King Mongkut’s University of Technology Thonburi

 

 

Woranich Hinthong, Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy

 

 

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การตรวจจับคลื่นสมอง Sleep spindle แบบ อัตโนมัติ

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Published

2022-12-29

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1.
Promkaew S, Kitudom P, Suwansombat I, Thongbun T, Thavornwattana K, Prom-on S, Hinthong W. A Review on Automatic Detection Methods of Sleep Spindles. J Chulabhorn Royal Acad [Internet]. 2022 Dec. 29 [cited 2024 Dec. 26];5(1):13-26. Available from: https://he02.tci-thaijo.org/index.php/jcra/article/view/255126

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Vol. 5 No. 1 (2023): January - March

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Academic Articles

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