Lateral Preoptic Hypothalamus: A Window to Understanding Insomnia

Taksokhan, Anita; Kim, Kyungwook

doi:10.1523/jneurosci.1560-22.2022

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…The recommended value of D ∈ [3,7] and τ = 1 [50]. Consequently, a sequence of Dlength vectors is formed from the original time series using the delay τ.…”

Section: Permutation Entropymentioning

confidence: 99%

“…Clinically, the comprehensive characterisation of a patient’s sleep architecture, including the distribution and duration of the different sleep stages through the course of the night, is fundamental for diagnosing a diverse set of sleep disorders. For instance, disruptions in sleep stage patterns in a patient are integral diagnostic criteria for insomnia [ 5 , 6 , 7 ], sleep apnoea [ 8 , 9 ], and narcolepsy [ 10 , 11 ]. Moreover, sleep stage classification is not only essential for diagnosing these disorders but also for monitoring the efficacy of various treatments.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Temporal Feature Extraction and Machine Learning for Classification of Sleep Stages Using Telemetry Polysomnography

Lal,

Mathavu Vasanthsena,

Hoblidar

2023

Brain Sciences

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Accurate sleep stage detection is crucial for diagnosing sleep disorders and tailoring treatment plans. Polysomnography (PSG) is considered the gold standard for sleep assessment since it captures a diverse set of physiological signals. While various studies have employed complex neural networks for sleep staging using PSG, our research emphasises the efficacy of a simpler and more efficient architecture. We aimed to integrate a diverse set of feature extraction measures with straightforward machine learning, potentially offering a more efficient avenue for sleep staging. We also aimed to conduct a comprehensive comparative analysis of feature extraction measures, including the power spectral density, Higuchi fractal dimension, singular value decomposition entropy, permutation entropy, and detrended fluctuation analysis, coupled with several machine-learning models, including XGBoost, Extra Trees, Random Forest, and LightGBM. Furthermore, data augmentation methods like the Synthetic Minority Oversampling Technique were also employed to rectify the inherent class imbalance in sleep data. The subsequent results highlighted that the XGBoost classifier, when used with a combination of all feature extraction measures as an ensemble, achieved the highest performance, with accuracies of 87%, 90%, 93%, 96%, and 97% and average F1-scores of 84.6%, 89%, 90.33%, 93.5%, and 93.5% for distinguishing between five-stage, four-stage, three-stage, and two distinct two-stage sleep configurations, respectively. This combined feature extraction technique represents a novel addition to the body of research since it achieves higher performance than many recently developed deep neural networks by utilising simpler machine-learning models.

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“…The recommended value of D ∈ [3,7] and τ = 1 [50]. Consequently, a sequence of Dlength vectors is formed from the original time series using the delay τ.…”

Section: Permutation Entropymentioning

confidence: 99%