LGSleepNet: An Automatic Sleep Staging Model Based on Local and Global Representation Learning

Qi, Shanshan; Xin, Junchang; Liu, Xinyao; Wang, Zhongyang; Li, Chuangang; Huang, Zhi-Hong; Wang, Zhiqiong

doi:10.1109/tim.2023.3298639

Cited by 11 publications

(2 citation statements)

References 51 publications

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“…Despite numerous instances of waking up during the sleep period, the subject promptly transitioned back into a sleep state after each awakening. We also compared the results with the latest published research works like LGSleepNet [ 5 ] SleepEEGNet [ 79 ], TinySleepNet [ 87 ], XSleepNet [ 88 ], CoSleepNet [ 89 ], SSleepNet [ 90 ], and RobustSleepNet [ 91 ] based on the multi-modal signal fusions and the same dataset. As observed in Tables 33 , 34 , and 35 , our proposed sleep staging classification method has demonstrated superior performance compared to other methods across three datasets.…”

Section: Discussionmentioning

confidence: 99%

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Machine learning-empowered sleep staging classification using multi-modality signals

Satapathy,

Brahma,

Panda

et al. 2024

BMC Med Inform Decis Mak

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The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), were considered to obtain the optimal fusions of the PSG signals, where 63 features were extracted. These include frequency-based, time-based, statistical-based, entropy-based, and non-linear-based features. We adopted the ReliefF (ReF) feature selection algorithms to find the suitable parts for each signal and superposition of PSG signals. Twelve top features were selected while correlated with the extracted feature sets' sleep stages. The selected features were fed into the AdaBoost with Random Forest (ADB + RF) classifier to validate the chosen segments and classify the sleep stages. This study's experiments were investigated by obtaining two testing schemes: epoch-wise testing and subject-wise testing. The suggested research was conducted using three publicly available datasets: ISRUC-Sleep subgroup1 (ISRUC-SG1), sleep-EDF(S-EDF), Physio bank CAP sleep database (PB-CAPSDB), and S-EDF-78 respectively. This work demonstrated that the proposed fusion strategy overestimates the common individual usage of PSG signals.

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Section: Discussionmentioning

confidence: 99%

“…Stages S1 to S4 are considered non-REM sleep stages. In later research, the American Academy of Sleep Medicine (AASM) introduced updated guidelines, consolidating the sleep cycle into five stages: Wakefulness (W), N1, N2, and N3, with changes reflecting the measurement and treatment of S3 and S4 as part of the N3 stage [ 5 ].…”

Section: Introductionmentioning

confidence: 99%