Transparency in Sleep Staging: Deep Learning Method for EEG Sleep Stage Classification with Model Interpretability
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913195426316288 |
|---|---|
| author | Sharma, Shivam Maiti, Suvadeep Mythirayee, S. Rajendran, Srijithesh Bapi, Raju Surampudi |
| author_facet | Sharma, Shivam Maiti, Suvadeep Mythirayee, S. Rajendran, Srijithesh Bapi, Raju Surampudi |
| contents | Automated Sleep stage classification using raw single channel EEG is a critical tool for sleep quality assessment and disorder diagnosis. However, modelling the complexity and variability inherent in this signal is a challenging task, limiting their practicality and effectiveness in clinical settings. To mitigate these challenges, this study presents an end-to-end deep learning (DL) model which integrates squeeze and excitation blocks within the residual network to extract features and stacked Bi-LSTM to understand complex temporal dependencies. A distinctive aspect of this study is the adaptation of GradCam for sleep staging, marking the first instance of an explainable DL model in this domain with alignment of its decision-making with sleep expert's insights. We evaluated our model on the publically available datasets (SleepEDF-20, SleepEDF-78, and SHHS), achieving Macro-F1 scores of 82.5, 78.9, and 81.9, respectively. Additionally, a novel training efficiency enhancement strategy was implemented by increasing stride size, leading to 8x faster training times with minimal impact on performance. Comparative analyses underscore our model outperforms all existing baselines, indicating its potential for clinical usage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_07156 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Transparency in Sleep Staging: Deep Learning Method for EEG Sleep Stage Classification with Model Interpretability Sharma, Shivam Maiti, Suvadeep Mythirayee, S. Rajendran, Srijithesh Bapi, Raju Surampudi Signal Processing Machine Learning Automated Sleep stage classification using raw single channel EEG is a critical tool for sleep quality assessment and disorder diagnosis. However, modelling the complexity and variability inherent in this signal is a challenging task, limiting their practicality and effectiveness in clinical settings. To mitigate these challenges, this study presents an end-to-end deep learning (DL) model which integrates squeeze and excitation blocks within the residual network to extract features and stacked Bi-LSTM to understand complex temporal dependencies. A distinctive aspect of this study is the adaptation of GradCam for sleep staging, marking the first instance of an explainable DL model in this domain with alignment of its decision-making with sleep expert's insights. We evaluated our model on the publically available datasets (SleepEDF-20, SleepEDF-78, and SHHS), achieving Macro-F1 scores of 82.5, 78.9, and 81.9, respectively. Additionally, a novel training efficiency enhancement strategy was implemented by increasing stride size, leading to 8x faster training times with minimal impact on performance. Comparative analyses underscore our model outperforms all existing baselines, indicating its potential for clinical usage. |
| title | Transparency in Sleep Staging: Deep Learning Method for EEG Sleep Stage Classification with Model Interpretability |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2309.07156 |