Discrete Wavelet Transform based statistical features for the Drowsiness detection from EEG

Vamsi, Reddy; Suman, D.; Ch, Nikhil; Mudigonda, Malini

doi:10.1007/978-981-10-4220-1_17

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…A significant decrease was observed in the levels of beta, alpha, theta activities within parietal, temporal and occipital lobe by other researchers [14][15][16][17][21][22] . However a quantitative approach to drowsiness state was attempted in this study.…”

Section: Discussionmentioning

confidence: 82%

ANN based Joint Time and frequency analysis of EEG for detection of driver drowsiness

et al. 2017

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Drowsiness detection plays a vital role in accidents avoidance systems, thereby saving many precious lives. Many attempts were made to detect the drowsiness by the physiological features such as Electroencephalogram (EEG), Electrooculogram (EOG), and Heart Rate Variability, etc., but a reliable index to determine the drowsiness is not yet a reality. This study contributes in identifying the drowsiness levels by an index called Drowsiness Index (DI) from the EEG signal analysis of the drivers. In this report, the EEG signal is processed to detect the behavioural patterns of the brain and drowsiness state of the drivers while performing monotonous driving for long distances. An eight-channel EEG data acquisition system is used to acquire the EEG data from thirteen male volunteers. The EEG signal is pre-processed and decomposed into various rhythms by applying a digital filter in MATLAB 2007b. Time-Frequency domain analysis has been done to extract certain features namely power within spectrogram, power within the root mean square deviation which are statistically significant (ρ < 0.05) in the detection of drowsiness. The driving profile is classified into active and drowsy by a separable marker with a range of 0.4-0.6, and linear regression analysis has been performed on the features extracted. A Drowsiness index is proposed stating a positive correlation (0.8-0.9) between the Total mean and the drowsy mean of the subject. The final features extracted from the data are classified using an ANN-based classifier system and has achieved a sensitivity of 99.82 per cent and specificity of 99.78 per cent.

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Section: Discussionmentioning

confidence: 82%

ANN based Joint Time and frequency analysis of EEG for detection of driver drowsiness

et al. 2017

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Driver Distraction From the EEG Perspective: A Review

Li,

Yuan,

Ouyang

et al. 2024

IEEE Sensors J.

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Drowsiness Detection by the Systems Dynamic Approach of Oculomotor Systems

Suman

Mudigonda

Reddy

et al. 2022

International Journal of Biomedical and Clinical Engineering

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literature shows that Blink Rate, Blink Duration, and Percentage of Eye Closure (PERCLOS) are the indicators of drowsiness, but the quantification of these parameters, inter-individual differences, and scientific or the physiological validation of the results have not been addressed. This study attempts to resolve these problems by the systems dynamic approach by modelling the oculomotor system. Autoregressive model of the EOG blink signatures during active and drowsy states are used to approximate and model the system. The impulse response of the active blink signal shows under damped response with the damping ratio of 0.61-0.75, (p<0.0005), and Drowsy blink signal shows a critically damped behavior with the damping ratio of 1, (p<0.0005). It is Clinically correlated that the continuous bombarding of the neuronal impulses from the brain acts as the stimulus for the blink, Hence during the drowsy phase, the response of the Oculomotor system is sluggish (Damping Ratio is high) thus causing increased Blink duration.

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Discrete Wavelet Transform based statistical features for the Drowsiness detection from EEG

Cited by 3 publications

References 15 publications

ANN based Joint Time and frequency analysis of EEG for detection of driver drowsiness

ANN based Joint Time and frequency analysis of EEG for detection of driver drowsiness

Driver Distraction From the EEG Perspective: A Review

Drowsiness Detection by the Systems Dynamic Approach of Oculomotor Systems

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