Deng, Yangfan
(2025)
Hierarchical and Explainable Feature Selection Framework for Dimensionality Reduction in Sleep Staging.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
Sleep is crucial for human health, and EEG signals play a significant role in sleep research. Due to the high-dimensional nature of EEG signal data sequences, data visualization and clustering of different sleep stages have been challenging. To address these issues, this thesis proposes a two-stage, hierarchical and explainable feature selection framework by incorporating a feature selection algorithm to improve the performance of dimensionality reduction. Inspired by topological data analysis (TDA), which can analyze the structure of high-dimensional data, we extracted topological features from the EEG signals to compensate for the structural information loss that happens in traditional spectro-temporal data analysis. Supported by the topological visualization of the data from different sleep stages and the classification results, the proposed features were proven to be effective supplements to traditional features. Finally, we compared the performances of three dimensionality reduction algorithms: Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor
Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). Among them, t-SNE achieved the highest accuracy of 79.8%, but considering the overall performance
in terms of computational resources and metrics, UMAP is the optimal choice.
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| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
7 January 2025 |
| Date Type: |
Publication |
| Defense Date: |
4 November 2024 |
| Approval Date: |
7 January 2025 |
| Submission Date: |
13 November 2024 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
50 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Electrical and Computer Engineering |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
EEG; Dimensionality Reduction; Topological Data Analysis; |
| Date Deposited: |
07 Jan 2025 21:11 |
| Last Modified: |
07 Jan 2025 21:11 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/47077 |
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