Optimal Feature Search for Vigilance Estimation Using Deep Reinforcement Learning
| Autor: | Cheolsoo Park, Bosun Hwang, Heejun Lee, Minsoo Yeo, Woojoon Seok, Taeheum Cho, Jiwoo You |
|---|---|
| Přispěvatelé: | Intelligent Systems |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Feature engineering
AUTOMATED DETECTION deep q-network Computer Networks and Communications Computer science media_common.quotation_subject 0206 medical engineering Feature extraction LEVEL lcsh:TK7800-8360 02 engineering and technology Electroencephalography electrocardiogram Convolutional neural network 0202 electrical engineering electronic engineering information engineering medicine Reinforcement learning EEG SLEEPINESS optimal feature selection Electrical and Electronic Engineering media_common medicine.diagnostic_test business.industry feature extraction COMPONENTS VARIANCE lcsh:Electronics Pattern recognition electroencephalogram vigilance estimation 020601 biomedical engineering DRIVER DROWSINESS Alertness Hardware and Architecture Control and Systems Engineering Signal Processing 020201 artificial intelligence & image processing Artificial intelligence NEURAL-NETWORKS business Vigilance (psychology) |
| Zdroj: | Electronics, Vol 9, Iss 1, p 142 (2020) Electronics world, 9(1):142. MDPI AG Electronics Volume 9 Issue 1 |
| ISSN: | 2079-9292 0959-8332 |
| Popis: | A low level of vigilance is one of the main reasons for traffic and industrial accidents. We conducted experiments to evoke the low level of vigilance and record physiological data through single-channel electroencephalogram (EEG) and electrocardiogram (ECG) measurements. In this study, a deep Q-network (DQN) algorithm was designed, using conventional feature engineering and deep convolutional neural network (CNN) methods, to extract the optimal features. The DQN yielded the optimal features: two CNN features from ECG and two conventional features from EEG. The ECG features were more significant for tracking the transitions within the alertness continuum with the DQN. The classification was performed with a small number of features, and the results were similar to those from using all of the features. This suggests that the DQN could be applied to investigating biomarkers for physiological responses and optimizing the classification system to reduce the input resources. |
| Databáze: | OpenAIRE |
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