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Shivam Kumar Sharma

Shivam Kumar Sharma supervised by Prof. Bapiraju Surampudi received his Master of Science  in Computer Science and Engineering (CSE). Here’s a summary of his research work on Supervised and Self-Supervised Approaches for Automated Sleep Stage Classification:

Sleep staging using Electroencephalogram (EEG) signals is crucial for assessing sleep quality and diagnosing related disorders. This thesis explores methodologies for automated sleep stage classification leveraging EEG data, focusing on both supervised learning and self-supervised learning (mulEEG). The mulEEG study introduces a novel multi-view self-supervised method designed for EEG representation learning. The mulEEG framework utilizes complementary information from multiple views to learn robust representations without requiring labeled data. Key contributions of mulEEG include an EEG augmentation strategy tailored for multi-view self-supervised learning, the introduction of a diverse loss function to enhance complementary information across views, and demonstrating the superiority of mulEEG over traditional supervised methods. The framework achieves this by combining different EEG augmentations such as jittering, flipping, and scaling, and processing these augmentations through both time-series and spectrogram encoders to capture diverse features. This joint training strategy, coupled with a unique contrastive loss function, significantly improves representation learning, making mulEEG highly effective for tasks like sleep staging. Results indicate that the mulEEG method outperforms supervised models in transfer learning scenarios, showing improvements of 1.1% in Cohen’s kappa and 0.85% in accuracy compared to traditional supervised learning models. In a second contribution, we explored supervised learning strategy for EEG sleep stage classification integrating squeeze and excitation (SE) blocks within a residual network and stacked Bidirectional Long Short-Term Memory (Bi-LSTM) units. This model aims to capture complex temporal dependencies and spatial features in EEG data. A significant aspect of this strategy is the application of GradCam for model interpretability, allowing visualization of the model’s decision-making process and allows comparison with sleep experts’ insights. The model was evaluated on several publicly available datasets (SleepEDF-20, SleepEDF-78, and SHHS), achieving high Macro-F1 scores of 82.5, 78.9, and 81.9, respectively. This demonstrates the model’s robustness and applicability across different datasets. Additionally, the study introduces a training efficiency enhancement strategy, reducing training times by eight-fold with minimal impact on performance. In conclusion, these EEG-based sleep staging approaches hold promise for enhancing sleep quality assessment and facilitating the diagnosis of sleep-related disorders, significantly contributing to the field of biomedical informatics. The incorporation of model interpretability provides valuable insights into the underlying decision-making processes, effectively bridging the gap between machine learning models and clinical practice.

October 2024