Investigating the contribution of distance-based features to automatic sleep stage classification.
Ali Abdollahi Gharbali1, Shirin Najdi1, José Manuel Fonseca1
1CTS, Uninova, 2829-516, Caparica, Portugal; Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa Campus da Caparica, Quinta da Torre, 2829-516, Monte de Caparica, Portugal.
Computers in Biology and Medicine
|March 13, 2018
Summary
New distance-based features improve automatic sleep stage classification accuracy. These features enhance the discrimination between REM and N1 sleep stages, a common challenge in sleep analysis.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Sleep Medicine
Background:
- Automatic sleep stage classification is crucial for diagnosing sleep disorders.
- Conventional features often struggle with discriminating between similar sleep stages like REM and N1.
- Novel feature extraction methods are needed to improve classification performance.
Purpose of the Study:
- To investigate the contribution of distance-based features to automatic sleep stage classification.
- To analyze the effectiveness of distance-based features individually and combined with conventional features.
- To enhance the accuracy and discriminative power of sleep stage classification systems.
Main Methods:
- Extracted 32 distance-based features using Itakura, Itakura-Saito, and COSH distances on EEG, EOG, EMG, and ECG signals.
- Evaluated distance-based, conventional, and combined feature sets using kNN, ANN, and DSVM classifiers.
- Employed six ranking methods and t-tests to assess feature discrimination ability for five sleep stages (Wake, REM, N1, N2, N3).
Main Results:
- Distance-based features constituted 25% of top-ranked features.
- Combining distance-based and conventional features improved classification accuracy.
- The DSVM classifier achieved the highest accuracy of 85.5% with 13 selected features from the combined set.
- Distance-based features showed superior performance in discriminating between REM and N1 stages.
Conclusions:
- Distance-based features offer a significant positive contribution to automatic sleep stage classification.
- Incorporating distance-based features enhances overall classification accuracy.
- These features improve the challenging discrimination between REM and N1 sleep stages.
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