Automating the process of human alertness classification has received special attention recently. Many applications can benefit from automatic alertness state identification, such as driver fatigue detection, monotonous task workers' vigilance detection and sleep studies in the medical field. Biological signals, such as Electrocardiogram (ECG), Electromyogram (EMG), and Electroencephalogram (EEG), along with human behaviors, such as head position, eye movement, have been used to infer the alertness state. Among all these, the EEG has been recognized as the best tool for this purpose. EEG is a complex signal that contains a wealth of information about brain' activities. It is, however, vulnerable to noise and artifacts. Dimensionality reduction techniques, such as feature extraction and/or feature selection, can lead to a small number of robust features that can be used in many applications such automatic classifications. Entropy-based features have been widely used in EEG signal analysis. A new class of entropy measure, called Range Entropy (RangeEn), was recently proposed to address weaknesses of two widely used entropy measures, namely Approximate Entropy (ApEn) and Sample Entropy (SampEn). This paper aims at investigating the ability of RangeEn in discriminating between EEG behaviors associated with different human alertness states, namely awake, drowsy, and asleep.
|Name||Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020|
- Approximate Entropy
- Human alertness states
- Range Entropy
- Sample Entropy