An algorithm for automatic detection of drowsiness for use in wearable EEG systems

Patrick, Kwai C. A.; Imtiaz, Syed Anas; Bowyer, Stuart A; Rodriguez‐Villegas, Esther

doi:10.1109/embc.2016.7591488

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…Sleep has been analyzed by different authors for multiple contexts, that is, to avoid accidents during sleepwalking ( Damkliang et al, 2019 ) or on roads ( Chowdhury et al, 2019 ; Patrick et al, 2016 ) to understand sleep behavior and patterns ( Budak et al, 2019 ; Hunter et al, 2021 ; Zhang et al, 2022 ), to measure sleep quality ( Hunter et al, 2021 ), detection of sleep stages ( Gaiduk et al, 2018 ), related diseases ( Mitsukura et al, 2020 ; Zhang et al, 2022 ), etc . For example, Damkliang et al (2019) worked on the detection of the sleepwalking algorithm with three classes (No, Slow, Quick) that were part of the awake state of sleep.…”

Section: Literature Surveymentioning

confidence: 99%

Sleep stage prediction using multimodal body network and circadian rhythm

Waqar,

Ghani Khan

2024

PeerJ Computer Science

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Quality sleep plays a vital role in living beings as it contributes extensively to the healing process and the removal of waste products from the body. Poor sleep may lead to depression, memory deficits, heart, and metabolic problems, etc. Sleep usually works in cycles and repeats itself by transitioning into different stages of sleep. This study is unique in that it uses wearable devices to collect multiple parameters from subjects and uses this information to predict sleep stages and sleep patterns. For the multivariate multiclass sleep stage prediction problem, we have experimented with both memoryless (ML) and memory-based models on seven database instances, that is, five from the collected dataset and two from the existing datasets. The Random Forest classifier outclassed the ML models that are LR, MLP, kNN, and SVM with accuracy (ACC) of 0.96 and Cohen Kappa 0.96, and the memory-based model long short-term memory (LSTM) performed well on all the datasets with the maximum attained accuracy of 0.88 and Kappa 0.82. The proposed methodology was also validated on a longitudinal dataset, the Multiethnic Study of Atherosclerosis (MESA), with ACC and Kappa of 0.75 and 0.64 for ML models and 0.86 and 0.78 for memory-based models, respectively, and from another benchmarked Apple Watch dataset available on Physio-Net with ACC and Kappa of 0.93 and 0.93 for ML and 0.92 and 0.87 for memory-based models, respectively. The given methodology showed better results than the original work and indicates that the memory-based method works better to capture the sleep pattern.

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