EEG and HRV markers of sleepiness and loss of control during car driving

Michail, Emmanouil; Kokonozi, Athina; Chouvarda, Ioanna; Maglaveras, Nicos

doi:10.1109/iembs.2008.4649724

Cited by 70 publications

(60 citation statements)

References 10 publications

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“…From ECG signals, the heart rate can be extracted from the interval between peaks (RR interval), which is used to calculate the heart rate variability (HRV). HRV can be used to investigate the physiological state of a person and detect the level of driver drowsiness [2], [3].…”

Section: Introductionmentioning

confidence: 99%

“…HRV is widely used to investigate the physiological state of a person [2], [3], [11]. It can be extracted from the spectral analysis of the heart rate, which can be obtained through many ways, such as ECG.…”

Section: Introductionmentioning

confidence: 99%

“…In [11], research is done to determine driver stress levels by using HRV. HRV analysis is applied on detection of driver drowsiness in [2] and [3], however the classification method used is just thresholding.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evolvable Block-based Neural Networks for classification of driver drowsiness based on heart rate variability

Nambiar

Khalil-Hani

Sia

et al. 2012

2012 IEEE International Conference on Circuits and Systems (ICCAS)

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Studies have shown that driver drowsiness is one of the main causes of road accidents. It is estimated that 30% of road accidents are caused by driver drowsiness, which creates a need for driver drowsiness detection in modern vehicle systems. Previous works have shown the viability of using heart rate variability (HRV) for detecting the onset of driver drowsiness. HRV is obtained for electrocardiogram (ECG) signals, of which the power bands can be analysed to determine the physiological state of a person. This paper introduces a new method to detect driver drowsiness by classifying the power spectrum of a person's HRV data using Block-based Neural Networks (BbNN), which is evolved using Genetic Algorithm (GA). For most cases, regular Artificial Neural Networks (ANN) are not suitable for high speed and efficient hardware implementation. BbNNs are better candidates due to its regular block based structure, has relatively fast computational speeds, lower resource consumption, and equal classifying strength in comparison to other ANNs. Preliminary work has shown promising results with up to 99.99% classification accuracy using the proposed BbNN detection system for HRV data.Index Terms-Block-based neural networks (BbNNs); genetic algorithm (GA); heart rate variability (HRV); electrocardiogram (ECG); driver drowsiness;

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evolvable Block-based Neural Networks for classification of driver drowsiness based on heart rate variability

Nambiar

Khalil-Hani

Sia

et al. 2012

2012 IEEE International Conference on Circuits and Systems (ICCAS)

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“…Recent research suggests that parasympathetic activation causes human errors, such as, errors while driving motorized vehicles [4][5][6]. Ikeda et al [1] assessed the possibility of a diagnostic technique called the full stomach test for identifying a high-risk group in patients having Brugada-type electrocardiograms (ECGs).…”

Section: Introductionmentioning

confidence: 99%

Non-contact determination of parasympathetic activation induced by a full stomach using microwave radar

Gotoh

Suzuki

Imuta

et al. 2009

Med Biol Eng Comput

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In order to evaluate parasympathetic activation which causes driving errors, without placing any burden on the monitored individuals, we conducted a non-contact parasympathetic activation monitoring through the back of a chair using a compact 24-GHz microwave-radar. We measured the high-frequency (HF, 0.15-0.4 Hz) power spectrum of heart rate variability (HRV) which reflects parasympathetic activation, induced by a full stomach. All participants had a large all-you-can-eat meal with beverages for lunch within 20 min. Before and after the large meals for durations of 10 min, the non-contact measurement was conducted for seven healthy male volunteers (mean age: 23 +/- 1-year-old). In both non-contact (microwave radar) and contact (ECG as a reference) measurement, HF shows similar variations before and after large meal. Large meal significantly (p < 0.05) increased non-contact-derived HF from 1,026 +/- 510 to 1,893 +/- 613 ms(2) (922 +/- 628 to 1,861 +/- 940 ms(2), p < 0.05). This technique allows parasympathetic activation monitoring for safety precautions.

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“…[7][8][9][10][11] In this paper, we focus mainly on two of the challenges that are: a) individual---focused decision support and b) short-term (1 minute interval) monitoring of in-vehicle driver stress based on EEG signals. The study includes EEG data collection in lab settings and in real road driving.…”

Section: Introductionmentioning

confidence: 99%

In-Vehicle Stress Monitoring Based on EEG Signal

Begum¹,

Ahmed²

2017

IJERA

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In recent years, improved road safety by monitoring human factors i.e., stress, mental load, sleepiness, fatigue etc. of vehicle drivers has been addressed in a number of studies. Due to the individual variations and complex dynamic in-vehicle environment systems that can monitor such factors of a driver while driving is challenging. This paper presents a drivers' stress monitoring system based on electroencephalography (EEG) signals enabling individual---focused computational approach that can generate automatic decision. Here, a combination of different signal processing i.e., discrete wavelet transform, largest Lyapunov exponent (LLE) and modified covariance have been applied to extract key features from the EEG signals. Hybrid classification approach Fuzzy-CBR (case-based reasoning) is used for decision support. The study has focused on both long and short-term temporal assessment of EEG signals enabling monitoring in different time intervals. In short time interval, which requires complex computations, the classification accuracy using the proposed approach is 79% compare to a human expert. Accuracy of EEG in developing such system is also compared with other reference signals e.g., Electrocardiography (ECG), Finger temperature, Skin conductance, and Respiration. The results show that in decision making the system can handle individual variations and provides decision in each minute time interval.

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EEG and HRV markers of sleepiness and loss of control during car driving

Cited by 70 publications

References 10 publications

Evolvable Block-based Neural Networks for classification of driver drowsiness based on heart rate variability

Evolvable Block-based Neural Networks for classification of driver drowsiness based on heart rate variability

Non-contact determination of parasympathetic activation induced by a full stomach using microwave radar

In-Vehicle Stress Monitoring Based on EEG Signal

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