Drowsiness, Fatigue and Poor Sleep’s Causes and Detection: A Comprehensive Study

Kamran, Muhammad Ahmad; Mannan, Malik Muhammad Naeem; Jeong, Myung Yung

doi:10.1109/access.2019.2951028

Cited by 36 publications

(14 citation statements)

References 121 publications

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“…Drowsiness is very closely related to sleep. Often, drowsiness is defined as a condition when someone is fighting against falling asleep [15]. Among the factors contributing to traffic accidents, a major factor is driver drowsiness [16].…”

Section: Introductionmentioning

confidence: 99%

A Drowsiness Reduction Strategy Utilizing Visual Stimulation With Different Colors of Light: An fNIRS Study

et al. 2021

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Drowsiness during driving is a severe problem that must be addressed to improve road safety. Numerous counter-measures have been proposed to resolve this issue like adaptive environmental settings (temperature, sound, and light). The objective of this study was to accurately predict the effects of exposure to different colors of light on human drowsiness by using functional near-infrared spectroscopy and other physical measurements (heart rate and eye closure). We targeted two regions of the brain (visual and prefrontal cortices). Twenty-three healthy subjects were investigated to evaluate all variables related to the awakening state, and twenty-one healthy subjects were also examined in the drowsy state evaluation. Eventually, the ten most suitable subjects were exposed to red, green, and blue lights under drowsy conditions, according to the experimental paradigm. Dim light was maintained in the experimental premises before and after colored light exposure to limit the results to those produced only in response to the desired stimuli. Eye closure, heart rate, and changes in oxy and deoxy hemoglobin concentrations were measured to characterize the condition (awake/drowsy) of the subject. A support vector machine classifier was used to identify the classification accuracy of awake and drowsy states. In conclusion, exposure to blue light triggered the activation of oxy hemoglobin in targeted brain regions; however, deoxy hemoglobin was not significantly affected by exposure to any of the colored lights. Noticeably, our study revealed that blue light exposure is more effective at reducing drowsiness than exposure to red and green lights.INDEX TERMS functional near-infrared spectroscopy, colored light exposure, drowsiness, sleep deprivation, heart rate, and eye closure.

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Section: Introductionmentioning

confidence: 99%

A Drowsiness Reduction Strategy Utilizing Visual Stimulation With Different Colors of Light: An fNIRS Study

et al. 2021

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“…EEG is usually described by its frequency components. The slow waves include the activities that are at the frequency in the range of 0.5-2.0 Hz, where the minimum amplitude of positive and negative peak-to-peak values recorded by the frontal leads is 75 mv, and also δ(0.5-4 Hz), θ (4-8 Hz), α (8-12 Hz), and β(12-35 Hz) waves (Kamran et al, 2019).…”

Section: Sleep Stagingmentioning

confidence: 99%

Automatic Sleep Staging Algorithm Based on Time Attention Mechanism

Feng

Wang

et al. 2021

Front. Hum. Neurosci.

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The most important part of sleep quality assessment is the automatic classification of sleep stages. Sleep staging is helpful in the diagnosis of sleep-related diseases. This study proposes an automatic sleep staging algorithm based on the time attention mechanism. Time-frequency and non-linear features are extracted from the physiological signals of six channels and then normalized. The time attention mechanism combined with the two-way bi-directional gated recurrent unit (GRU) was used to reduce computing resources and time costs, and the conditional random field (CRF) was used to obtain information between tags. After five-fold cross-validation on the Sleep-EDF dataset, the values of accuracy, WF1, and Kappa were 0.9218, 0.9177, and 0.8751, respectively. After five-fold cross-validation on the our own dataset, the values of accuracy, WF1, and Kappa were 0.9006, 0.8991, and 0.8664, respectively, which is better than the result of the latest algorithm. In the study of sleep staging, the recognition rate of the N1 stage was low, and the imbalance has always been a problem. Therefore, this study introduces a type of balancing strategy. By adopting the proposed strategy, SEN-N1 and ACC of 0.7 and 0.86, respectively, can be achieved. The experimental results show that compared to the latest method, the proposed model can achieve significantly better performance and significantly improve the recognition rate of the N1 period. The performance comparison of different channels shows that even when the EEG channel was not used, considerable accuracy can be obtained.

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“…Drowsiness is the intermediate state between awareness and sleep [13][14][15]. Terms like tiredness and sleepiness are used as synonyms for drowsiness [16][17][18]. Some researchers also use fatigue as synonymous with drowsiness [19,20].…”

Section: Introductionmentioning

confidence: 99%

A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems

Stančin¹,

Cifrek²,

Jović³

2021

Sensors

126

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Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection.

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Drowsiness, Fatigue and Poor Sleep’s Causes and Detection: A Comprehensive Study

Cited by 36 publications

References 121 publications

A Drowsiness Reduction Strategy Utilizing Visual Stimulation With Different Colors of Light: An fNIRS Study

A Drowsiness Reduction Strategy Utilizing Visual Stimulation With Different Colors of Light: An fNIRS Study

Automatic Sleep Staging Algorithm Based on Time Attention Mechanism

A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems

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