MSSC-BiMamba: Multimodal Sleep Stage Classification and Early Diagnosis of Sleep Disorders with Bidirectional Mamba

Chao Zhang, Weirong Cui, Jingjing Guo·May 30, 2024

Summary

The paper presents MSSC-BiMamba, a multimodal sleep stage classification model that combines an Efficient Channel Attention (ECA) mechanism with a Bidirectional State Space Model (BSSM) to enhance accuracy and efficiency in sleep disorder detection. The model addresses traditional PSG limitations and improves upon transformer models by effectively handling diverse sleep conditions. MSSC-BiMamba demonstrates high accuracy on ISRUC-S3, ISRUC-S1, and Sleep-EDF datasets, with its bidirectional approach enhancing performance and memory management. The model's potential lies in real-time and large-scale sleep health monitoring, making it a promising tool for accessible and accurate sleep disorder diagnosis. The study also compares different approaches and highlights the advancements in automatic sleep staging systems, from traditional methods to deep learning, with a focus on data-driven feature selection and model performance.

Key findings

5

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper "MSSC-BiMamba: Multimodal Sleep Stage Classification and Early Diagnosis of Sleep Disorders with Bidirectional Mamba" aims to address the challenge of sleep stage classification using polysomnographic (PSG) data and determining the health status of sleep participants . This paper introduces an innovative model architecture, MSSC-BiMamba, which combines the Efficient Channel Attention mechanism with BiMamba to enhance diagnostic accuracy and efficiency in sleep medicine . While the classification of sleep stages traditionally required manual assessment by sleep specialists, this paper leverages machine learning algorithms to automate the process, reducing the time and effort needed for classification . The use of deep learning methods like CNNs, RNNs, and GCNs has advanced the automation of sleep stage classification, with this paper proposing a novel approach that focuses on time-domain signals for superior performance . The problem addressed in this paper is not entirely new, as previous studies have explored automated sleep stage classification using various methods, but the innovative model architecture and approach presented in this paper contribute to the ongoing advancements in this field .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that the developed automated model for sleep staging and disorder classification, MSSC-BiMamba, enhances diagnostic accuracy and efficiency in monitoring sleep states, evaluating sleep quality, and diagnosing sleep disorders . The model combines an Efficient Channel Attention (ECA) mechanism with a Bidirectional State Space Model (BSSM) to effectively classify sleep stages and discriminate health status based on polysomnography (PSG) multi-lead sleep monitoring data . The goal is to bridge the gap between intricate clinical evaluations and practical, real-time monitoring solutions, making advanced healthcare interventions more accessible and promoting better sleep health management through innovative technology .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "MSSC-BiMamba: Multimodal Sleep Stage Classification and Early Diagnosis of Sleep Disorders with Bidirectional Mamba" introduces several innovative ideas, methods, and models :

  1. Innovative Model Architecture: The paper introduces the MSSC-BiMamba model, which combines the Efficient Channel Attention mechanism with BiMamba for sleep stage classification and health status discrimination. This model significantly improves computational and memory efficiency, bridging the gap between clinical evaluations and real-time monitoring solutions .

  2. Efficient Channel Attention Module: The paper discusses the Efficient Channel Attention (ECA) module, which dynamically adjusts channel responses of feature maps by learning the importance of each channel. This module effectively captures cross-channel interactions, enhancing the representational capacity of neural networks. The ECA mechanism is tailored to handle time series data, adjusting multiple PSG channel data to capture temporal patterns and dependencies more effectively .

  3. State Space Model and Mamba: The research explores state-space models (SSMs) and the Mamba algorithm, which integrates time-varying parameters into SSMs for efficient training and inference. The Bidirectional Mamba model offers higher efficiency and performance compared to the original Mamba. This approach leverages temporal PSG signals for sleep stage classification tasks .

  4. Deep Learning Methods: The paper highlights the use of deep learning methods, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Convolutional Networks (GCNs), for sleep stage classification tasks. While CNNs struggle with capturing long-term dependencies, RNNs are effective but prone to gradient issues, and GCNs exhibit lower efficiency in processing large-scale graph data. The proposed approach in the paper focuses on utilizing time-domain signals for superior performance .

  5. Model Performance and Efficiency: The MSSC-BiMamba model outperforms previous models in terms of computational efficiency and memory usage. It demonstrates high performance metrics on datasets like ISRUC-S1 and ISRUC-S3 for sleep stage classification and sleep health detection. The model's efficiency makes it suitable for deployment in clinical settings with limited computational resources .

Overall, the paper presents a comprehensive framework that combines innovative model architecture, efficient channel attention mechanisms, state space models, and deep learning methods to enhance sleep stage classification and early diagnosis of sleep disorders. The MSSC-BiMamba model proposed in the paper introduces several key characteristics and advantages compared to previous methods:

  1. Innovative Model Architecture: The MSSC-BiMamba model combines the Efficient Channel Attention mechanism with BiMamba, specifically designed for sleep stage classification and health status discrimination. This innovative architecture significantly enhances computational and memory efficiency, bridging the gap between complex clinical evaluations and practical, real-time monitoring solutions .

  2. Efficient Channel Attention Module: The model incorporates the Efficient Channel Attention (ECA) module, which dynamically adjusts channel responses of feature maps by learning the importance of each channel. This mechanism effectively captures cross-channel interactions, ensuring both efficiency and effectiveness in handling time series data for sleep stage classification tasks .

  3. Superior Performance Metrics: The MSSC-BiMamba model demonstrates superior performance on datasets like ISRUC-S1 and ISRUC-S3 for sleep stage classification. It achieves a high accuracy of 0.952 on the Sleep-EDF153 and ISRUC datasets for sleep health detection, confirming its generalizability and effectiveness across various sleep-related tasks .

  4. Efficiency and Generalizability: Compared to previous models, the MSSC-BiMamba model exhibits higher efficiency with significantly reduced parameter count, making it more suitable for deployment in clinical settings with limited computational resources. The model's efficiency, combined with its robust performance metrics, highlights its potential for diverse sleep stage classification tasks .

  5. Deep Learning Advancements: The MSSC-BiMamba model leverages deep learning methods, focusing on time-domain signals for superior performance in sleep stage classification tasks. By directly extracting complex features from raw data, the model reduces the preprocessing workload and enhances classification accuracy .

Overall, the MSSC-BiMamba model stands out for its innovative architecture, efficient channel attention mechanism, superior performance metrics, efficiency, and deep learning advancements, making it a promising approach for enhancing sleep stage classification and early diagnosis of sleep disorders in clinical environments.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of sleep stage classification and early diagnosis of sleep disorders. Noteworthy researchers in this field include L. Peter-Derex, P. Yammine, H. Bastuji, B. Croisile , H. W. Loh, C. P. Ooi, J. Vicnesh, S. L. Oh, O. Faust, A. Gertych, U. R. Acharya , A. Gu, T. Dao , R. Sharma, R. B. Pachori, A. Upadhyay , L. Zoubek, S. Charbonnier, S. Lesecq, A. Buguet, F. Chapotot , and R. N. Sekkal, F. Bereksi-Reguig, D. Ruiz-Fernandez, N. Dib, S. Sekkal .

The key solution mentioned in the paper is the development of the MSSC-BiMamba model, which combines an Efficient Channel Attention (ECA) mechanism with a Bidirectional State Space Model (BSSM) to automate sleep staging and disorder classification. This model enhances diagnostic accuracy and efficiency by effectively capturing the multidimensional features and long-range dependencies of polysomnography (PSG) data. The ECA module allows for weighting data from different sensor channels, while the implementation of bidirectional Mamba (BiMamba) enables the model to handle diverse sleep conditions and improve computational and memory efficiency .


How were the experiments in the paper designed?

The experiments in the paper were designed with a focus on evaluating the performance of the proposed MSSC-BiMamba model for sleep stage classification and early diagnosis of sleep disorders. The experiments involved:

  • Training the model on different datasets such as ISRUC-S1 and ISRUC-S3 .
  • Utilizing cross-validation experiments to assess the generalization ability of the model across different datasets by training on ISRUC-S1(50) and ISRUC-S3 datasets and testing on subsets like S1(50), S1(100), and the entire S3 dataset .
  • Comparing the model's performance on different subsets to evaluate its generalization capability and effectiveness in classifying sleep stages across diverse health conditions .
  • Analyzing the results using metrics such as F1-score, accuracy, and kappa values to determine the model's performance across various sleep stages and datasets .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the ISRUC-S3 dataset and the ISRUC-S1 dataset . The code used in the study is based on Pytorch version 2.1.1 and is not explicitly mentioned to be open source in the provided context .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study developed an automated model, MSSC-BiMamba, for sleep staging and disorder classification, aiming to enhance diagnostic accuracy and efficiency . The model demonstrated impressive performance on sleep stage classification tasks on datasets containing data with healthy and unhealthy sleep patterns . Additionally, the model exhibited high accuracy for sleep health prediction when evaluated on combined datasets, confirming its generalizability and effectiveness across different sleep-related tasks . The research findings showcase the potential of the model to accurately classify sleep stages and predict sleep health issues, thereby contributing significantly to the field of sleep medicine .


What are the contributions of this paper?

The paper "MSSC-BiMamba: Multimodal Sleep Stage Classification and Early Diagnosis of Sleep Disorders with Bidirectional Mamba" presents several key contributions:

  • Innovative Model Architecture: The paper introduces the MSSC-BiMamba model with a unique architecture that combines the Efficient Channel Attention mechanism with BiMamba, specifically tailored for sleep stage classification and health status discrimination .
  • Enhanced Efficiency: The MSSC-BiMamba model significantly improves computational and memory efficiency by utilizing complex multimodal PSG data, bridging the gap between intricate clinical evaluations and practical, real-time monitoring solutions .
  • Superior Results: The model demonstrates superior performance on various datasets for sleep stage classification and sleep health detection, achieving a high accuracy of 0.952 on specific datasets, confirming its generalizability and effectiveness across different sleep-related tasks .

What work can be continued in depth?

Further work in this area can focus on exploring the potential of the Bidirectional Mamba model in enhancing prediction accuracy for sequential tasks, particularly in challenging stages like N1 with limited sample sizes. Research could investigate the impact of different model depths on performance and efficiency, as increasing the depth of the model does not always lead to improved results across all sleep stages . Additionally, there is room to delve deeper into the adaptation of the Efficient Channel Attention (ECA) module for time series data, optimizing its effectiveness in capturing temporal patterns and dependencies for more accurate sleep stage classification .

Tables

4
Basic info
papers
artificial intelligence
Advanced features
Insights
What is the primary focus of MSSC-BiMamba presented in the paper?
What are the key features of MSSC-BiMamba that contribute to its improved accuracy and efficiency in sleep stage classification?
How does the model MSSC-BiMamba address the limitations of traditional PSG systems?
In which datasets does MSSC-BiMamba demonstrate high accuracy, and what is the significance of its bidirectional approach?

MSSC-BiMamba: Multimodal Sleep Stage Classification and Early Diagnosis of Sleep Disorders with Bidirectional Mamba

Chao Zhang, Weirong Cui, Jingjing Guo·May 30, 2024

Summary

The paper presents MSSC-BiMamba, a multimodal sleep stage classification model that combines an Efficient Channel Attention (ECA) mechanism with a Bidirectional State Space Model (BSSM) to enhance accuracy and efficiency in sleep disorder detection. The model addresses traditional PSG limitations and improves upon transformer models by effectively handling diverse sleep conditions. MSSC-BiMamba demonstrates high accuracy on ISRUC-S3, ISRUC-S1, and Sleep-EDF datasets, with its bidirectional approach enhancing performance and memory management. The model's potential lies in real-time and large-scale sleep health monitoring, making it a promising tool for accessible and accurate sleep disorder diagnosis. The study also compares different approaches and highlights the advancements in automatic sleep staging systems, from traditional methods to deep learning, with a focus on data-driven feature selection and model performance.
Mind map
Sleep-EDF
ISRUC-S1
ISRUC-S3
Advancements in Automatic Sleep Staging Systems
Model Training and Evaluation
Bidirectional State Space Model (BSSM)
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Method
Introduction
Key findings
5

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper "MSSC-BiMamba: Multimodal Sleep Stage Classification and Early Diagnosis of Sleep Disorders with Bidirectional Mamba" aims to address the challenge of sleep stage classification using polysomnographic (PSG) data and determining the health status of sleep participants . This paper introduces an innovative model architecture, MSSC-BiMamba, which combines the Efficient Channel Attention mechanism with BiMamba to enhance diagnostic accuracy and efficiency in sleep medicine . While the classification of sleep stages traditionally required manual assessment by sleep specialists, this paper leverages machine learning algorithms to automate the process, reducing the time and effort needed for classification . The use of deep learning methods like CNNs, RNNs, and GCNs has advanced the automation of sleep stage classification, with this paper proposing a novel approach that focuses on time-domain signals for superior performance . The problem addressed in this paper is not entirely new, as previous studies have explored automated sleep stage classification using various methods, but the innovative model architecture and approach presented in this paper contribute to the ongoing advancements in this field .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that the developed automated model for sleep staging and disorder classification, MSSC-BiMamba, enhances diagnostic accuracy and efficiency in monitoring sleep states, evaluating sleep quality, and diagnosing sleep disorders . The model combines an Efficient Channel Attention (ECA) mechanism with a Bidirectional State Space Model (BSSM) to effectively classify sleep stages and discriminate health status based on polysomnography (PSG) multi-lead sleep monitoring data . The goal is to bridge the gap between intricate clinical evaluations and practical, real-time monitoring solutions, making advanced healthcare interventions more accessible and promoting better sleep health management through innovative technology .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "MSSC-BiMamba: Multimodal Sleep Stage Classification and Early Diagnosis of Sleep Disorders with Bidirectional Mamba" introduces several innovative ideas, methods, and models :

  1. Innovative Model Architecture: The paper introduces the MSSC-BiMamba model, which combines the Efficient Channel Attention mechanism with BiMamba for sleep stage classification and health status discrimination. This model significantly improves computational and memory efficiency, bridging the gap between clinical evaluations and real-time monitoring solutions .

  2. Efficient Channel Attention Module: The paper discusses the Efficient Channel Attention (ECA) module, which dynamically adjusts channel responses of feature maps by learning the importance of each channel. This module effectively captures cross-channel interactions, enhancing the representational capacity of neural networks. The ECA mechanism is tailored to handle time series data, adjusting multiple PSG channel data to capture temporal patterns and dependencies more effectively .

  3. State Space Model and Mamba: The research explores state-space models (SSMs) and the Mamba algorithm, which integrates time-varying parameters into SSMs for efficient training and inference. The Bidirectional Mamba model offers higher efficiency and performance compared to the original Mamba. This approach leverages temporal PSG signals for sleep stage classification tasks .

  4. Deep Learning Methods: The paper highlights the use of deep learning methods, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Convolutional Networks (GCNs), for sleep stage classification tasks. While CNNs struggle with capturing long-term dependencies, RNNs are effective but prone to gradient issues, and GCNs exhibit lower efficiency in processing large-scale graph data. The proposed approach in the paper focuses on utilizing time-domain signals for superior performance .

  5. Model Performance and Efficiency: The MSSC-BiMamba model outperforms previous models in terms of computational efficiency and memory usage. It demonstrates high performance metrics on datasets like ISRUC-S1 and ISRUC-S3 for sleep stage classification and sleep health detection. The model's efficiency makes it suitable for deployment in clinical settings with limited computational resources .

Overall, the paper presents a comprehensive framework that combines innovative model architecture, efficient channel attention mechanisms, state space models, and deep learning methods to enhance sleep stage classification and early diagnosis of sleep disorders. The MSSC-BiMamba model proposed in the paper introduces several key characteristics and advantages compared to previous methods:

  1. Innovative Model Architecture: The MSSC-BiMamba model combines the Efficient Channel Attention mechanism with BiMamba, specifically designed for sleep stage classification and health status discrimination. This innovative architecture significantly enhances computational and memory efficiency, bridging the gap between complex clinical evaluations and practical, real-time monitoring solutions .

  2. Efficient Channel Attention Module: The model incorporates the Efficient Channel Attention (ECA) module, which dynamically adjusts channel responses of feature maps by learning the importance of each channel. This mechanism effectively captures cross-channel interactions, ensuring both efficiency and effectiveness in handling time series data for sleep stage classification tasks .

  3. Superior Performance Metrics: The MSSC-BiMamba model demonstrates superior performance on datasets like ISRUC-S1 and ISRUC-S3 for sleep stage classification. It achieves a high accuracy of 0.952 on the Sleep-EDF153 and ISRUC datasets for sleep health detection, confirming its generalizability and effectiveness across various sleep-related tasks .

  4. Efficiency and Generalizability: Compared to previous models, the MSSC-BiMamba model exhibits higher efficiency with significantly reduced parameter count, making it more suitable for deployment in clinical settings with limited computational resources. The model's efficiency, combined with its robust performance metrics, highlights its potential for diverse sleep stage classification tasks .

  5. Deep Learning Advancements: The MSSC-BiMamba model leverages deep learning methods, focusing on time-domain signals for superior performance in sleep stage classification tasks. By directly extracting complex features from raw data, the model reduces the preprocessing workload and enhances classification accuracy .

Overall, the MSSC-BiMamba model stands out for its innovative architecture, efficient channel attention mechanism, superior performance metrics, efficiency, and deep learning advancements, making it a promising approach for enhancing sleep stage classification and early diagnosis of sleep disorders in clinical environments.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of sleep stage classification and early diagnosis of sleep disorders. Noteworthy researchers in this field include L. Peter-Derex, P. Yammine, H. Bastuji, B. Croisile , H. W. Loh, C. P. Ooi, J. Vicnesh, S. L. Oh, O. Faust, A. Gertych, U. R. Acharya , A. Gu, T. Dao , R. Sharma, R. B. Pachori, A. Upadhyay , L. Zoubek, S. Charbonnier, S. Lesecq, A. Buguet, F. Chapotot , and R. N. Sekkal, F. Bereksi-Reguig, D. Ruiz-Fernandez, N. Dib, S. Sekkal .

The key solution mentioned in the paper is the development of the MSSC-BiMamba model, which combines an Efficient Channel Attention (ECA) mechanism with a Bidirectional State Space Model (BSSM) to automate sleep staging and disorder classification. This model enhances diagnostic accuracy and efficiency by effectively capturing the multidimensional features and long-range dependencies of polysomnography (PSG) data. The ECA module allows for weighting data from different sensor channels, while the implementation of bidirectional Mamba (BiMamba) enables the model to handle diverse sleep conditions and improve computational and memory efficiency .


How were the experiments in the paper designed?

The experiments in the paper were designed with a focus on evaluating the performance of the proposed MSSC-BiMamba model for sleep stage classification and early diagnosis of sleep disorders. The experiments involved:

  • Training the model on different datasets such as ISRUC-S1 and ISRUC-S3 .
  • Utilizing cross-validation experiments to assess the generalization ability of the model across different datasets by training on ISRUC-S1(50) and ISRUC-S3 datasets and testing on subsets like S1(50), S1(100), and the entire S3 dataset .
  • Comparing the model's performance on different subsets to evaluate its generalization capability and effectiveness in classifying sleep stages across diverse health conditions .
  • Analyzing the results using metrics such as F1-score, accuracy, and kappa values to determine the model's performance across various sleep stages and datasets .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the ISRUC-S3 dataset and the ISRUC-S1 dataset . The code used in the study is based on Pytorch version 2.1.1 and is not explicitly mentioned to be open source in the provided context .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study developed an automated model, MSSC-BiMamba, for sleep staging and disorder classification, aiming to enhance diagnostic accuracy and efficiency . The model demonstrated impressive performance on sleep stage classification tasks on datasets containing data with healthy and unhealthy sleep patterns . Additionally, the model exhibited high accuracy for sleep health prediction when evaluated on combined datasets, confirming its generalizability and effectiveness across different sleep-related tasks . The research findings showcase the potential of the model to accurately classify sleep stages and predict sleep health issues, thereby contributing significantly to the field of sleep medicine .


What are the contributions of this paper?

The paper "MSSC-BiMamba: Multimodal Sleep Stage Classification and Early Diagnosis of Sleep Disorders with Bidirectional Mamba" presents several key contributions:

  • Innovative Model Architecture: The paper introduces the MSSC-BiMamba model with a unique architecture that combines the Efficient Channel Attention mechanism with BiMamba, specifically tailored for sleep stage classification and health status discrimination .
  • Enhanced Efficiency: The MSSC-BiMamba model significantly improves computational and memory efficiency by utilizing complex multimodal PSG data, bridging the gap between intricate clinical evaluations and practical, real-time monitoring solutions .
  • Superior Results: The model demonstrates superior performance on various datasets for sleep stage classification and sleep health detection, achieving a high accuracy of 0.952 on specific datasets, confirming its generalizability and effectiveness across different sleep-related tasks .

What work can be continued in depth?

Further work in this area can focus on exploring the potential of the Bidirectional Mamba model in enhancing prediction accuracy for sequential tasks, particularly in challenging stages like N1 with limited sample sizes. Research could investigate the impact of different model depths on performance and efficiency, as increasing the depth of the model does not always lead to improved results across all sleep stages . Additionally, there is room to delve deeper into the adaptation of the Efficient Channel Attention (ECA) module for time series data, optimizing its effectiveness in capturing temporal patterns and dependencies for more accurate sleep stage classification .

Tables
4
Scan the QR code to ask more questions about the paper
© 2025 Powerdrill. All rights reserved.