Deep Learning Application to Clinical Decision Support System in Sleep Stage Classification

Kim, Dong‐Young; Lee, Jeong-Gun; Woo, Yunhee; Jeong, Jaemin; Kim, Chulho; Kim, Chang Woo

doi:10.3390/jpm12020136

Cited by 25 publications

(9 citation statements)

References 43 publications

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“…In the context of EEG-based applications, Transformer models have been explored for tasks such as imagined speech recognition, emotion detection, and sleep stage identification [42]- [44]. A limited number of studies have attempted to apply Transformer-based models specifically to motor imagery EEG (MI-EEG).…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Potential of Attention Mechanism-Based Deep Learning for Robust Subject-Independent Motor-Imagery Based BCIs

Keutayeva,

Abibullaev

2023

IEEE Access

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This study explores the use of attention mechanism-based deep learning models to construct subject-independent motor-imagery based brain-computer interfaces (MI-BCIs), which present unique and intricate challenges from a machine learning perspective. By comparing four attention mechanism-based models and employing nested LOSO methods for robust model selection, the study enhances the reliability of performance estimates and offers unique insights into the application of attention mechanisms in building subject-independent BCIs. The results indicate the potential of the Spatio-Temporal CNN + ViT model for practical BCI applications, as it outperforms other models on several datasets. Additionally, the study presents a realistic approach to building subject-independent BCIs by combining attention mechanisms and deep learning models to identify informative features common across subjects while filtering out noise and irrelevant data. While there are limitations and areas for future work to enhance the potential of these models, transformer-based models could become even more valuable in the BCI research field, leading to more robust and accurate subject-independent BCIs for various applications. The need for subject-independent MI-BCIs is amplified due to their potential in assisting individuals with severe neurological conditions, such as ALS and locked-in syndrome, which severely limit mobility and communication.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring the Potential of Attention Mechanism-Based Deep Learning for Robust Subject-Independent Motor-Imagery Based BCIs

Keutayeva,

Abibullaev

2023

IEEE Access

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“…Sleep research can also benefit from BCI methods, particularly in the classification of sleep stages-a pivotal aspect of diagnosing sleep disorders [102]. Using EEG data, machine learning algorithms can discern sleep stages such as Wake, REM, and the non-REM stages (N1, N2, N3) with pronounced accuracy [103].…”

Section: B Emerging Applicationsmentioning

confidence: 99%

Deep Learning in EEG-Based BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications

Abibullaev,

Keutayeva,

Zollanvari

2023

IEEE Access

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Brain-computer interfaces (BCIs) have undergone significant advancements in recent years. The integration of deep learning techniques, specifically transformers, has shown promising development in research and application domains. Transformers, which were originally designed for natural language processing, have now made notable inroads into BCIs, offering a unique self-attention mechanism that adeptly handles the temporal dynamics of brain signals. This comprehensive survey delves into the application of transformers in BCIs, providing readers with a lucid understanding of their foundational principles, inherent advantages, potential challenges, and diverse applications. In addition to discussing the benefits of transformers, we also address their limitations, such as computational overhead, interpretability concerns, and the data-intensive nature of these models, providing a well-rounded analysis. Furthermore, the paper sheds light on the myriad of BCI applications that have benefited from the incorporation of transformers. These applications span from motor imagery decoding, emotion recognition, and sleep stage analysis to novel ventures such as speech reconstruction. This review serves as a holistic guide for researchers and practitioners, offering a panoramic view of the transformative potential of transformers in the BCI landscape. With the inclusion of examples and references, readers will gain a deeper understanding of the topic and its significance in the field.

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“…The authors of [ 24 ] used the C4-M1 channel to develop clinical decision support systems (CDSSs). The authors proposed a deep learning model that utilized CNN and transformers to classify three sleep stages.…”

Section: Related Workmentioning

confidence: 99%

Influence of Channel Selection and Subject’s Age on the Performance of the Single Channel EEG-Based Automatic Sleep Staging Algorithms

Nazih

Shahin

Eldesouki

et al. 2023

Sensors

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The electroencephalogram (EEG) signal is a key parameter used to identify the different sleep stages present in an overnight sleep recording. Sleep staging is crucial in the diagnosis of several sleep disorders; however, the manual annotation of the EEG signal is a costly and time-consuming process. Automatic sleep staging algorithms offer a practical and cost-effective alternative to manual sleep staging. However, due to the limited availability of EEG sleep datasets, the reliability of existing sleep staging algorithms is questionable. Furthermore, most reported experimental results have been obtained using adult EEG signals; the effectiveness of these algorithms using pediatric EEGs is unknown. In this paper, we conduct an intensive study of two state-of-the-art single-channel EEG-based sleep staging algorithms, namely DeepSleepNet and AttnSleep, using a recently released large-scale sleep dataset collected from 3984 patients, most of whom are children. The paper studies how the performance of these sleep staging algorithms varies when applied on different EEG channels and across different age groups. Furthermore, all results were analyzed within individual sleep stages to understand how each stage is affected by the choice of EEG channel and the participants’ age. The study concluded that the selection of the channel is crucial for the accuracy of the single-channel EEG-based automatic sleep staging methods. For instance, channels O1-M2 and O2-M1 performed consistently worse than other channels for both algorithms and through all age groups. The study also revealed the challenges in the automatic sleep staging of newborns and infants (1–52 weeks).

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Deep Learning Application to Clinical Decision Support System in Sleep Stage Classification

Cited by 25 publications

References 43 publications

Exploring the Potential of Attention Mechanism-Based Deep Learning for Robust Subject-Independent Motor-Imagery Based BCIs

Exploring the Potential of Attention Mechanism-Based Deep Learning for Robust Subject-Independent Motor-Imagery Based BCIs

Deep Learning in EEG-Based BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications

Influence of Channel Selection and Subject’s Age on the Performance of the Single Channel EEG-Based Automatic Sleep Staging Algorithms

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