A Transformer-Based Spatial-Temporal Sleep Staging Model Through Raw EEG

Shi, Guang; Chen, Zheng; Zhang, Renyuan

doi:10.1109/hpbdis53214.2021.9658439

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Inspired by the dynamic nature of the human brain and its changing characteristics during sleep, Shi et al [195] developed an innovative Transformer-based model for fully automated sleep scoring. When evaluating their model using the Sleep Heart Health Study dataset, they achieved excellent classification results, with F1-scores of 0.92, 0.85, and 0.84 for the Wake, N2, and N3 stages, respectively.…”

Section: Sleep Monitoringmentioning

confidence: 99%

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Comprehensive review of Transformer‐based models in neuroscience, neurology, and psychiatry

Cong,

Wang,

Zhou

et al. 2024

Brain-X

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This comprehensive review aims to clarify the growing impact of Transformer‐based models in the fields of neuroscience, neurology, and psychiatry. Originally developed as a solution for analyzing sequential data, the Transformer architecture has evolved to effectively capture complex spatiotemporal relationships and long‐range dependencies that are common in biomedical data. Its adaptability and effectiveness in deciphering intricate patterns within medical studies have established it as a key tool in advancing our understanding of neural functions and disorders, representing a significant departure from traditional computational methods. The review begins by introducing the structure and principles of Transformer architectures. It then explores their applicability, ranging from disease diagnosis and prognosis to the evaluation of cognitive processes and neural decoding. The specific design modifications tailored for these applications and their subsequent impact on performance are also discussed. We conclude by providing a comprehensive assessment of recent advancements, prevailing challenges, and future directions, highlighting the shift in neuroscientific research and clinical practice towards an artificial intelligence‐centric paradigm, particularly given the prominence of Transformer architecture in the most successful large pre‐trained models. This review serves as an informative reference for researchers, clinicians, and professionals who are interested in understanding and harnessing the transformative potential of Transformer‐based models in neuroscience, neurology, and psychiatry.

show abstract

Section: Sleep Monitoringmentioning

confidence: 99%

“…Inspired by the dynamic nature of the human brain and its changing characteristics during sleep, Shi et al [195] . developed an innovative Transformer‐based model for fully automated sleep scoring.…”

Section: Transformer In Spatiotemporal Sequence Analysismentioning

confidence: 99%

Comprehensive review of Transformer‐based models in neuroscience, neurology, and psychiatry

Cong,

Wang,

Zhou

et al. 2024

Brain-X

View full text Add to dashboard Cite

show abstract

“…In [22], Seo et al proposed IITNet, which utilized Bi-LSTM to explore features inside and between the sleep stages. Drawing lessons from the transformer's idea of selfattention, the transform-based models [23], [24] can learn the features of sequence signals more effectively and have higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Temporal-Spectral Fused and Attention-Based Deep Model for Automatic Sleep Staging

Zhou

Gong

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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EEG Convolutional Sparse Transformer for Epilepsy Detection and Related Drug Classification

Chen

et al. 2023

Lecture Notes in Computer Science

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A Transformer-Based Spatial-Temporal Sleep Staging Model Through Raw EEG

Cited by 5 publications

References 14 publications

Comprehensive review of Transformer‐based models in neuroscience, neurology, and psychiatry

Comprehensive review of Transformer‐based models in neuroscience, neurology, and psychiatry

A Temporal-Spectral Fused and Attention-Based Deep Model for Automatic Sleep Staging

EEG Convolutional Sparse Transformer for Epilepsy Detection and Related Drug Classification

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