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Abstract

Objective: In this paper, the contribution of distance-based features to automatic sleep stage classification is investigated. The potency of these features is analyzed individually and in combination with 48 conventionally used features. Methods: The distance-based set consists of 32 features extracted by calculating Itakura, Itakura-Saito and COSH distances of autoregressive and spectral coefficients of Electrocardiography (EEG) (C3-A2), Left EOG, Chin EMG and ECG signals. All the evaluations are performed on three feature sets: distance-based, conventional and total (combined distance based and conventional). Six ranking methods were used to find the top features with the highest discrimination ability in each set. The ranked feature lists were evaluated using k-Nearest Neighbor (kNN), Artificial Neural Network (ANN), and Decision-tree-based multi-SVM (DSVM) classifiers for five sleep stages including Wake, REM, N1, N2 and N3. Furthermore, the ability of distance-based and conventional features to discriminate between each pair of sleep stages was evaluated using t-test, a hypothesis testing method. Results: Distance-based features occupied 25% of top-ranked features. Simulation results showed that using distance-based features together with conventional features can lead to an enhancement of accuracy. The best classification accuracy (85.5%) was achieved by DSVM classifier and 13 features selected by mRMR-MID and normalized with Min-Max method for total feature set, where two of them were from the distance-based feature set. The t-test results show that distance-based features outperform conventional features in discriminating between N1 and REM stages that is usually a challenge for classification systems. Conclusion: Distance-based features have a positive contribution to sleep stage classification, including enhancement of accuracy and better REM-N1 discrimination ability. Significance: The main motivation for this work was to evaluate new features to characterize each sleep stage in such a way that extracted features were more powerful than conventional features, to distinguish sleep stages from each other, and to improve classifiers accuracy.

Original languageEnglish
Pages (from-to)8-23
Number of pages16
JournalComputers in Biology and Medicine
Volume96
DOIs
Publication statusPublished - 1 May 2018

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Sleep Stages
Aptitude
Classifiers
Electrocardiography
Electrooculography
Chin
Decision Trees
Decision trees
varespladib methyl
Sleep
Motivation
Neural networks
Testing

Keywords

  • Distance-based features
  • Feature extraction
  • Feature selection
  • Itakura
  • Itakura-saito
  • Polysomnography
  • Sleep stage classification

Cite this

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title = "Investigating the contribution of distance-based features to automatic sleep stage classification",
abstract = "Objective: In this paper, the contribution of distance-based features to automatic sleep stage classification is investigated. The potency of these features is analyzed individually and in combination with 48 conventionally used features. Methods: The distance-based set consists of 32 features extracted by calculating Itakura, Itakura-Saito and COSH distances of autoregressive and spectral coefficients of Electrocardiography (EEG) (C3-A2), Left EOG, Chin EMG and ECG signals. All the evaluations are performed on three feature sets: distance-based, conventional and total (combined distance based and conventional). Six ranking methods were used to find the top features with the highest discrimination ability in each set. The ranked feature lists were evaluated using k-Nearest Neighbor (kNN), Artificial Neural Network (ANN), and Decision-tree-based multi-SVM (DSVM) classifiers for five sleep stages including Wake, REM, N1, N2 and N3. Furthermore, the ability of distance-based and conventional features to discriminate between each pair of sleep stages was evaluated using t-test, a hypothesis testing method. Results: Distance-based features occupied 25{\%} of top-ranked features. Simulation results showed that using distance-based features together with conventional features can lead to an enhancement of accuracy. The best classification accuracy (85.5{\%}) was achieved by DSVM classifier and 13 features selected by mRMR-MID and normalized with Min-Max method for total feature set, where two of them were from the distance-based feature set. The t-test results show that distance-based features outperform conventional features in discriminating between N1 and REM stages that is usually a challenge for classification systems. Conclusion: Distance-based features have a positive contribution to sleep stage classification, including enhancement of accuracy and better REM-N1 discrimination ability. Significance: The main motivation for this work was to evaluate new features to characterize each sleep stage in such a way that extracted features were more powerful than conventional features, to distinguish sleep stages from each other, and to improve classifiers accuracy.",
keywords = "Distance-based features, Feature extraction, Feature selection, Itakura, Itakura-saito, Polysomnography, Sleep stage classification",
author = "Gharbali, {Ali Abdollahi} and Shirin Najdi and Fonseca, {Jos{\'e} Manuel}",
note = "This work was partially funded by FCT Strategic Program UID/EEA/00066/203 of UNINOVA, CTS. Sem PDF conforme despacho.",
year = "2018",
month = "5",
day = "1",
doi = "10.1016/j.compbiomed.2018.03.001",
language = "English",
volume = "96",
pages = "8--23",
journal = "Computers in Biology and Medicine",
issn = "0010-4825",
publisher = "Elsevier Limited",

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TY - JOUR

T1 - Investigating the contribution of distance-based features to automatic sleep stage classification

AU - Gharbali, Ali Abdollahi

AU - Najdi, Shirin

AU - Fonseca, José Manuel

N1 - This work was partially funded by FCT Strategic Program UID/EEA/00066/203 of UNINOVA, CTS. Sem PDF conforme despacho.

PY - 2018/5/1

Y1 - 2018/5/1

N2 - Objective: In this paper, the contribution of distance-based features to automatic sleep stage classification is investigated. The potency of these features is analyzed individually and in combination with 48 conventionally used features. Methods: The distance-based set consists of 32 features extracted by calculating Itakura, Itakura-Saito and COSH distances of autoregressive and spectral coefficients of Electrocardiography (EEG) (C3-A2), Left EOG, Chin EMG and ECG signals. All the evaluations are performed on three feature sets: distance-based, conventional and total (combined distance based and conventional). Six ranking methods were used to find the top features with the highest discrimination ability in each set. The ranked feature lists were evaluated using k-Nearest Neighbor (kNN), Artificial Neural Network (ANN), and Decision-tree-based multi-SVM (DSVM) classifiers for five sleep stages including Wake, REM, N1, N2 and N3. Furthermore, the ability of distance-based and conventional features to discriminate between each pair of sleep stages was evaluated using t-test, a hypothesis testing method. Results: Distance-based features occupied 25% of top-ranked features. Simulation results showed that using distance-based features together with conventional features can lead to an enhancement of accuracy. The best classification accuracy (85.5%) was achieved by DSVM classifier and 13 features selected by mRMR-MID and normalized with Min-Max method for total feature set, where two of them were from the distance-based feature set. The t-test results show that distance-based features outperform conventional features in discriminating between N1 and REM stages that is usually a challenge for classification systems. Conclusion: Distance-based features have a positive contribution to sleep stage classification, including enhancement of accuracy and better REM-N1 discrimination ability. Significance: The main motivation for this work was to evaluate new features to characterize each sleep stage in such a way that extracted features were more powerful than conventional features, to distinguish sleep stages from each other, and to improve classifiers accuracy.

AB - Objective: In this paper, the contribution of distance-based features to automatic sleep stage classification is investigated. The potency of these features is analyzed individually and in combination with 48 conventionally used features. Methods: The distance-based set consists of 32 features extracted by calculating Itakura, Itakura-Saito and COSH distances of autoregressive and spectral coefficients of Electrocardiography (EEG) (C3-A2), Left EOG, Chin EMG and ECG signals. All the evaluations are performed on three feature sets: distance-based, conventional and total (combined distance based and conventional). Six ranking methods were used to find the top features with the highest discrimination ability in each set. The ranked feature lists were evaluated using k-Nearest Neighbor (kNN), Artificial Neural Network (ANN), and Decision-tree-based multi-SVM (DSVM) classifiers for five sleep stages including Wake, REM, N1, N2 and N3. Furthermore, the ability of distance-based and conventional features to discriminate between each pair of sleep stages was evaluated using t-test, a hypothesis testing method. Results: Distance-based features occupied 25% of top-ranked features. Simulation results showed that using distance-based features together with conventional features can lead to an enhancement of accuracy. The best classification accuracy (85.5%) was achieved by DSVM classifier and 13 features selected by mRMR-MID and normalized with Min-Max method for total feature set, where two of them were from the distance-based feature set. The t-test results show that distance-based features outperform conventional features in discriminating between N1 and REM stages that is usually a challenge for classification systems. Conclusion: Distance-based features have a positive contribution to sleep stage classification, including enhancement of accuracy and better REM-N1 discrimination ability. Significance: The main motivation for this work was to evaluate new features to characterize each sleep stage in such a way that extracted features were more powerful than conventional features, to distinguish sleep stages from each other, and to improve classifiers accuracy.

KW - Distance-based features

KW - Feature extraction

KW - Feature selection

KW - Itakura

KW - Itakura-saito

KW - Polysomnography

KW - Sleep stage classification

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