Linear automatic detection of eye movements during the transition between wake and sleep

Hiroshige, Yoshiharu

doi:10.1046/j.1440-1819.1999.00528.x

Cited by 11 publications

(8 citation statements)

References 4 publications

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“…Several studies have specifically investigated SEM detection to identify sleep onset automatically (Atienza et al. , 2004; Hiroshige, 1999; Magosso et al. , 2006; Suzuki et al.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have specifically investigated SEM detection to identify sleep onset automatically (Atienza et al, 2004;Hiroshige, 1999;Magosso et al, 2006;Suzuki et al, 2001;Va¨rri et al, 1996;Virkkala et al, 2007). Va¨rri et al (1996) proposed a SEM detection method using non-linear filtering techniques and applied it to EOG data measured during evening activities before going to bed.…”

Section: Introductionmentioning

confidence: 99%

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Slow eye movement detection can prevent sleep-related accidents effectively in a simulated driving task

Shin

Sakai

Uchiyama

2010

Journal of Sleep Research

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SUMMARY A delayed response caused by sleepiness can result in severe car accidents. Previous studies suggest that slow eye movement (SEM) is a sleep-onset index related to delayed response. This study was undertaken to verify that SEM detection is effective for preventing sleep-related accidents. We propose a real-time detection algorithm of SEM based on feature-extracted parameters of electrooculogram (EOG), i.e. amplitude and mean velocity of eye movement. In Experiment 1, 12 participants (33.5 ± 7.3 years) performed an auditory detection task with EOG measurement to determine the threshold parameters of the proposed algorithm. Consequently, the valid threshold parameters were determined, respectively, as >5°and <30°s. In Experiment 2, 11 participants (32.8 ± 7.2 years) performed a simulated car-following task to verify that the SEM detection is effective for preventing sleep-related accidents. Accidents in the SEM condition were significantly more numerous than in the non-SEM condition (P < 0.01, one-way repeated-measures anova followed by Scheffe´Õs test). Furthermore, no accident occurred in the SEM condition with a warning generated using the proposed algorithm. Results also demonstrate clearly that the SEM detection can prevent sleep-related accidents effectively in this simulated driving task.

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“…Several studies have specifically investigated SEM detection to identify sleep onset automatically (Atienza et al. , 2004; Hiroshige, 1999; Magosso et al. , 2006; Suzuki et al.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Slow eye movement detection can prevent sleep-related accidents effectively in a simulated driving task

Shin

Sakai

Uchiyama

2010

Journal of Sleep Research

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“…However, VS has an inherent problem of individual bias. Although there have been several attempts at objective measurement of SEM and REM, there seems to be low agreement between quantitative scoring and VS. Hiroshige 1 recently developed a linear regression (LR) method for automatic detection of these movements and obtained high agreement with the results of VS. We have developed a template matching (TM) method for autodetection of these movements 2 . In the present study, we applied both methods to the same 20 min electro‐oculogram (EOG) data in the transition from wakefulness to sleep and compared the results with those from VS.…”

Section: Introductionmentioning

confidence: 82%

“…The LR was applied in each moving window for approximation to the tangent line on the EOG curve. 1 The TM used various templates of the altered sine waves. These were applied to the EOG curves, and the degree of similarity was calculated.…”

Section: Methodsmentioning

confidence: 99%

Two auto‐detection methods for eye movements during eyes closed

Suzuki

Moriguchi

Kojima

et al. 2001

Psychiatry Clin Neurosci

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Eye movements during closed eyes closely reflect changes of the arousal level during transition from wakefulness to sleep. Because they contain both rapid and slow eye movements (REM and SEM), it has been difficult to detect them automatically. Hiroshige recently developed the method of linear regression analysis for automatic detection of the two types of eye movements, and we have developed a template matching method for autodetection. The aim of the present study was to compare both auto-detection methods and visual scoring for REM and SEM. The results revealed high agreement between the two quantitative methods and the visual scoring, indicating that auto-detection of eye movements is useful for quantitative evaluation of arousal level.

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“…In sleep research field, there exist some methods, but these methods were are not satisfactory. In 1999, a linear regression method was used for the detection of SEM, and it was reported that the cycle length of SEM was shorter at stage wake than at sleep stages 1 and 2 [6]. In 2006, Elisa Magosso developed a wavelet based method, which was under the assumption that energy distribution was modified during SEM epochs according to the observation of experimental data [3].…”

Section: Introductionmentioning

confidence: 99%

Recognizing slow eye movement for driver fatigue detection with machine learning approach

Jiao

Peng

et al. 2014

2014 International Joint Conference on Neural Networks (IJCNN)

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Slow eye movement (SEM) regarded as a sign of onset of sleep is very significant for detecting driver fatigue, but its characteristics and detection algorithm have been rarely involved in the study of driver fatigue detection. In this study, some new features were extracted based on wavelet singularity analysis and statistics to detect SEMs. Six subjects participated in this simulated driving experiment, and for each subject, a more than 2 hours electro-oculogram (EOG) session was recorded. Each session was divided into SEM epochs and non-SEM epochs according to the common judgments made by the two of three experts by the visual recognition criteria of SEMs. Regarding the problem of detecting SEMs as an imbalance classification problem, and through the under-sampling and over-sampling methods a 2s horizontal electro-oculogram (HEO) signal could finally be recognized as the category of SEMs or non-SEMs with the classifiers SVM, GELM, and KNN respectively. Results prove that the proposed features was a little better than the wavelet energy features, and through the combination of the wavelet energy features and the new features based on wavelet singularity analysis and statistics, the classification results were improved obviously.

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Linear automatic detection of eye movements during the transition between wake and sleep

Cited by 11 publications

References 4 publications

Slow eye movement detection can prevent sleep-related accidents effectively in a simulated driving task

Slow eye movement detection can prevent sleep-related accidents effectively in a simulated driving task

Two auto‐detection methods for eye movements during eyes closed

Recognizing slow eye movement for driver fatigue detection with machine learning approach

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