Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm

Shanguang Zhao, Fangfang Long, Xin Wei, Xiaoli Ni, Hui Wang and Bokun Wei
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Shanguang Zhao: Centre for Sport and Exercise Sciences, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Fangfang Long: Department of Psychology, Nanjing University, Nanjing 210093, China
Xin Wei: Institute of Social Psychology, School of Humanities and Social Sciences, Xi’an Jiaotong University, Xi’an 710049, China
Xiaoli Ni: Institute of Social Psychology, School of Humanities and Social Sciences, Xi’an Jiaotong University, Xi’an 710049, China
Hui Wang: Department of the Psychology of Military Medicine, Air Force Medical University, Xi’an 710032, China
Bokun Wei: Xi’an Middle School of Shaanxi Province, Xi’an 710006, China

IJERPH, 2022, vol. 19, issue 5, 1-20

Abstract: Sleep staging is the basis of sleep assessment and plays a crucial role in the early diagnosis and intervention of sleep disorders. Manual sleep staging by a specialist is time-consuming and is influenced by subjective factors. Moreover, some automatic sleep staging algorithms are complex and inaccurate. The paper proposes a single-channel EEG-based sleep staging method that provides reliable technical support for diagnosing sleep problems. In this study, 59 features were extracted from three aspects: time domain, frequency domain, and nonlinear indexes based on single-channel EEG data. Support vector machine, neural network, decision tree, and random forest classifier were used to classify sleep stages automatically. The results reveal that the random forest classifier has the best sleep staging performance among the four algorithms. The recognition rate of the Wake phase was the highest, at 92.13%, and that of the N1 phase was the lowest, at 73.46%, with an average accuracy of 83.61%. The embedded method was adopted for feature filtering. The results of sleep staging of the 11-dimensional features after filtering show that the random forest model achieved 83.51% staging accuracy under the condition of reduced feature dimensions, and the coincidence rate with the use of all features for sleep staging was 94.85%. Our study confirms the robustness of the random forest model in sleep staging, which also represents a high classification accuracy with appropriate classifier algorithms, even using single-channel EEG data. This study provides a new direction for the portability of clinical EEG monitoring.

Keywords: EEG; sleep staging; support vector machine; decision tree; back propagation neural network; random forest (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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