A Review on Automatic Detection Methods of Sleep Spindles
Keywords:
Electroencephalography (EEG), Signal processing, Machine learning, Sleep spindle, Automatic detectionAbstract
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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