An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation
Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated proced...
Ausführliche Beschreibung
Autor*in: |
Dereymaeker, Anneleen [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2017 |
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Rechteinformationen: |
Nutzungsrecht: © 2017, The Author(s) |
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Übergeordnetes Werk: |
Enthalten in: International journal of neural systems - Singapore [u.a.] : World Scientific, 1989, 27(2017), 6 |
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Übergeordnetes Werk: |
volume:27 ; year:2017 ; number:6 |
Links: |
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DOI / URN: |
10.1142/S012906571750023X |
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Katalog-ID: |
OLC1998097439 |
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520 | |a Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ( PMA = gestational age + postnatal age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27–42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement Kappa = 0 . 9 3 ), using Sensitivity, Specificity, Detection Factor ( DF = proportion of visual QS periods correctly detected by CLASS) and Misclassification Factor ( MF = proportion of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31–38 weeks (median DF = 1 . 0 , median MF 0–0.25, median Sensitivity 0.93–1.0, and median Specificity 0.80–0.91 across this age range), with minimal misclassifications at 35–36 weeks (median MF = 0 ). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation. | ||
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700 | 1 | |a De Vos, Maarten |4 oth | |
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10.1142/S012906571750023X doi PQ20171125 (DE-627)OLC1998097439 (DE-599)GBVOLC1998097439 (PRQ)w77X-201bae81d0352dd39f5608a19503540d42532ae586f5ac9e08634785c94814f40 (KEY)0184614020170000027000600000automatedquietsleepdetectionapproachinpreterminfan DE-627 ger DE-627 rakwb eng 610 ZDB 54.00 bkl Dereymaeker, Anneleen verfasserin aut An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ( PMA = gestational age + postnatal age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27–42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement Kappa = 0 . 9 3 ), using Sensitivity, Specificity, Detection Factor ( DF = proportion of visual QS periods correctly detected by CLASS) and Misclassification Factor ( MF = proportion of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31–38 weeks (median DF = 1 . 0 , median MF 0–0.25, median Sensitivity 0.93–1.0, and median Specificity 0.80–0.91 across this age range), with minimal misclassifications at 35–36 weeks (median MF = 0 ). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation. Nutzungsrecht: © 2017, The Author(s) Pillay, Kirubin oth Vervisch, Jan oth Van Huffel, Sabine oth Naulaers, Gunnar oth Jansen, Katrien oth De Vos, Maarten oth Enthalten in International journal of neural systems Singapore [u.a.] : World Scientific, 1989 27(2017), 6 (DE-627)130833029 (DE-600)1017890-9 (DE-576)038685655 0129-0657 nnns volume:27 year:2017 number:6 http://dx.doi.org/10.1142/S012906571750023X Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_2006 54.00 AVZ AR 27 2017 6 |
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10.1142/S012906571750023X doi PQ20171125 (DE-627)OLC1998097439 (DE-599)GBVOLC1998097439 (PRQ)w77X-201bae81d0352dd39f5608a19503540d42532ae586f5ac9e08634785c94814f40 (KEY)0184614020170000027000600000automatedquietsleepdetectionapproachinpreterminfan DE-627 ger DE-627 rakwb eng 610 ZDB 54.00 bkl Dereymaeker, Anneleen verfasserin aut An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ( PMA = gestational age + postnatal age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27–42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement Kappa = 0 . 9 3 ), using Sensitivity, Specificity, Detection Factor ( DF = proportion of visual QS periods correctly detected by CLASS) and Misclassification Factor ( MF = proportion of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31–38 weeks (median DF = 1 . 0 , median MF 0–0.25, median Sensitivity 0.93–1.0, and median Specificity 0.80–0.91 across this age range), with minimal misclassifications at 35–36 weeks (median MF = 0 ). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation. Nutzungsrecht: © 2017, The Author(s) Pillay, Kirubin oth Vervisch, Jan oth Van Huffel, Sabine oth Naulaers, Gunnar oth Jansen, Katrien oth De Vos, Maarten oth Enthalten in International journal of neural systems Singapore [u.a.] : World Scientific, 1989 27(2017), 6 (DE-627)130833029 (DE-600)1017890-9 (DE-576)038685655 0129-0657 nnns volume:27 year:2017 number:6 http://dx.doi.org/10.1142/S012906571750023X Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_2006 54.00 AVZ AR 27 2017 6 |
allfields_unstemmed |
10.1142/S012906571750023X doi PQ20171125 (DE-627)OLC1998097439 (DE-599)GBVOLC1998097439 (PRQ)w77X-201bae81d0352dd39f5608a19503540d42532ae586f5ac9e08634785c94814f40 (KEY)0184614020170000027000600000automatedquietsleepdetectionapproachinpreterminfan DE-627 ger DE-627 rakwb eng 610 ZDB 54.00 bkl Dereymaeker, Anneleen verfasserin aut An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ( PMA = gestational age + postnatal age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27–42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement Kappa = 0 . 9 3 ), using Sensitivity, Specificity, Detection Factor ( DF = proportion of visual QS periods correctly detected by CLASS) and Misclassification Factor ( MF = proportion of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31–38 weeks (median DF = 1 . 0 , median MF 0–0.25, median Sensitivity 0.93–1.0, and median Specificity 0.80–0.91 across this age range), with minimal misclassifications at 35–36 weeks (median MF = 0 ). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation. Nutzungsrecht: © 2017, The Author(s) Pillay, Kirubin oth Vervisch, Jan oth Van Huffel, Sabine oth Naulaers, Gunnar oth Jansen, Katrien oth De Vos, Maarten oth Enthalten in International journal of neural systems Singapore [u.a.] : World Scientific, 1989 27(2017), 6 (DE-627)130833029 (DE-600)1017890-9 (DE-576)038685655 0129-0657 nnns volume:27 year:2017 number:6 http://dx.doi.org/10.1142/S012906571750023X Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_2006 54.00 AVZ AR 27 2017 6 |
allfieldsGer |
10.1142/S012906571750023X doi PQ20171125 (DE-627)OLC1998097439 (DE-599)GBVOLC1998097439 (PRQ)w77X-201bae81d0352dd39f5608a19503540d42532ae586f5ac9e08634785c94814f40 (KEY)0184614020170000027000600000automatedquietsleepdetectionapproachinpreterminfan DE-627 ger DE-627 rakwb eng 610 ZDB 54.00 bkl Dereymaeker, Anneleen verfasserin aut An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ( PMA = gestational age + postnatal age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27–42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement Kappa = 0 . 9 3 ), using Sensitivity, Specificity, Detection Factor ( DF = proportion of visual QS periods correctly detected by CLASS) and Misclassification Factor ( MF = proportion of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31–38 weeks (median DF = 1 . 0 , median MF 0–0.25, median Sensitivity 0.93–1.0, and median Specificity 0.80–0.91 across this age range), with minimal misclassifications at 35–36 weeks (median MF = 0 ). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation. Nutzungsrecht: © 2017, The Author(s) Pillay, Kirubin oth Vervisch, Jan oth Van Huffel, Sabine oth Naulaers, Gunnar oth Jansen, Katrien oth De Vos, Maarten oth Enthalten in International journal of neural systems Singapore [u.a.] : World Scientific, 1989 27(2017), 6 (DE-627)130833029 (DE-600)1017890-9 (DE-576)038685655 0129-0657 nnns volume:27 year:2017 number:6 http://dx.doi.org/10.1142/S012906571750023X Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_2006 54.00 AVZ AR 27 2017 6 |
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10.1142/S012906571750023X doi PQ20171125 (DE-627)OLC1998097439 (DE-599)GBVOLC1998097439 (PRQ)w77X-201bae81d0352dd39f5608a19503540d42532ae586f5ac9e08634785c94814f40 (KEY)0184614020170000027000600000automatedquietsleepdetectionapproachinpreterminfan DE-627 ger DE-627 rakwb eng 610 ZDB 54.00 bkl Dereymaeker, Anneleen verfasserin aut An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ( PMA = gestational age + postnatal age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27–42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement Kappa = 0 . 9 3 ), using Sensitivity, Specificity, Detection Factor ( DF = proportion of visual QS periods correctly detected by CLASS) and Misclassification Factor ( MF = proportion of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31–38 weeks (median DF = 1 . 0 , median MF 0–0.25, median Sensitivity 0.93–1.0, and median Specificity 0.80–0.91 across this age range), with minimal misclassifications at 35–36 weeks (median MF = 0 ). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation. Nutzungsrecht: © 2017, The Author(s) Pillay, Kirubin oth Vervisch, Jan oth Van Huffel, Sabine oth Naulaers, Gunnar oth Jansen, Katrien oth De Vos, Maarten oth Enthalten in International journal of neural systems Singapore [u.a.] : World Scientific, 1989 27(2017), 6 (DE-627)130833029 (DE-600)1017890-9 (DE-576)038685655 0129-0657 nnns volume:27 year:2017 number:6 http://dx.doi.org/10.1142/S012906571750023X Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_2006 54.00 AVZ AR 27 2017 6 |
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However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ( PMA = gestational age + postnatal age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27–42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement Kappa = 0 . 9 3 ), using Sensitivity, Specificity, Detection Factor ( DF = proportion of visual QS periods correctly detected by CLASS) and Misclassification Factor ( MF = proportion of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31–38 weeks (median DF = 1 . 0 , median MF 0–0.25, median Sensitivity 0.93–1.0, and median Specificity 0.80–0.91 across this age range), with minimal misclassifications at 35–36 weeks (median MF = 0 ). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. 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automated quiet sleep detection approach in preterm infants as a gateway to assess brain maturation |
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An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation |
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Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ( PMA = gestational age + postnatal age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27–42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement Kappa = 0 . 9 3 ), using Sensitivity, Specificity, Detection Factor ( DF = proportion of visual QS periods correctly detected by CLASS) and Misclassification Factor ( MF = proportion of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31–38 weeks (median DF = 1 . 0 , median MF 0–0.25, median Sensitivity 0.93–1.0, and median Specificity 0.80–0.91 across this age range), with minimal misclassifications at 35–36 weeks (median MF = 0 ). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation. |
abstractGer |
Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ( PMA = gestational age + postnatal age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27–42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement Kappa = 0 . 9 3 ), using Sensitivity, Specificity, Detection Factor ( DF = proportion of visual QS periods correctly detected by CLASS) and Misclassification Factor ( MF = proportion of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31–38 weeks (median DF = 1 . 0 , median MF 0–0.25, median Sensitivity 0.93–1.0, and median Specificity 0.80–0.91 across this age range), with minimal misclassifications at 35–36 weeks (median MF = 0 ). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation. |
abstract_unstemmed |
Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ( PMA = gestational age + postnatal age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27–42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement Kappa = 0 . 9 3 ), using Sensitivity, Specificity, Detection Factor ( DF = proportion of visual QS periods correctly detected by CLASS) and Misclassification Factor ( MF = proportion of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31–38 weeks (median DF = 1 . 0 , median MF 0–0.25, median Sensitivity 0.93–1.0, and median Specificity 0.80–0.91 across this age range), with minimal misclassifications at 35–36 weeks (median MF = 0 ). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation. |
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An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation |
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