Automatic video detection of body movement during sleep based on optical flow in pediatric patients with epilepsy
Abstract The aim of our work is to investigate whether the optical flow algorithm applied to video recordings can be used to detect movement during sleep in pediatric patients with epilepsy. The optical flow algorithm allocates intensities to pixels proportional to their involvement in movement of a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cuppens, Kris [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2010 |
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| Schlagwörter: |
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| Anmerkung: |
© International Federation for Medical and Biological Engineering 2010 |
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| Übergeordnetes Werk: |
Enthalten in: Medical & biological engineering & computing - Springer-Verlag, 1977, 48(2010), 9 vom: 24. Juni, Seite 923-931 |
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| Übergeordnetes Werk: |
volume:48 ; year:2010 ; number:9 ; day:24 ; month:06 ; pages:923-931 |
| Links: |
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| DOI / URN: |
10.1007/s11517-010-0648-4 |
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OLC2038687366 |
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| 245 | 1 | 0 | |a Automatic video detection of body movement during sleep based on optical flow in pediatric patients with epilepsy |
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| 520 | |a Abstract The aim of our work is to investigate whether the optical flow algorithm applied to video recordings can be used to detect movement during sleep in pediatric patients with epilepsy. The optical flow algorithm allocates intensities to pixels proportional to their involvement in movement of an object. The average of a percentage of the highest movement vectors was plotted as a function of time (R(t)). The used dataset contains video data acquired at the University Hospital of Leuven consisting of normal sleep movement and seizure movement. We investigated R(t), to make a distinction between movement and non-movement. We used the acquisition parameters (320 × 240 at 12.5 fps), derived from a previous study (Cuppens et al., Proceedings of the 4th European congress of the international federation for medical and biological engineering (MBEC 2008), ECIFBME 2008, Antwerp, Belgium, IFMBE Proceedings, vol 22, pp 784–789, 2008). Two experiments were concluded, one with global thresholds of R(t) in all datasets and one with a variable threshold in each dataset. The latter is obtained by inspecting a non-movement epoch and calculating the mean and standard deviations of R(t) over time. The variable threshold on R(t) was then obtained for each dataset by adding to the mean a fixed multiple of the standard deviation. Optimal thresholds were derived based on a three-fold cross-validation. The best result was achieved when using a variable threshold, which resulted in a sensitivity of one in all the test sets and a PPV of 1, 0.821, and 1, respectively, for the three test sets. | ||
| 650 | 4 | |a Epilepsy | |
| 650 | 4 | |a Pediatric patients | |
| 650 | 4 | |a Optical flow | |
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| 700 | 1 | |a Ceulemans, Berten |4 aut | |
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10.1007/s11517-010-0648-4 doi (DE-627)OLC2038687366 (DE-He213)s11517-010-0648-4-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Cuppens, Kris verfasserin aut Automatic video detection of body movement during sleep based on optical flow in pediatric patients with epilepsy 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2010 Abstract The aim of our work is to investigate whether the optical flow algorithm applied to video recordings can be used to detect movement during sleep in pediatric patients with epilepsy. The optical flow algorithm allocates intensities to pixels proportional to their involvement in movement of an object. The average of a percentage of the highest movement vectors was plotted as a function of time (R(t)). The used dataset contains video data acquired at the University Hospital of Leuven consisting of normal sleep movement and seizure movement. We investigated R(t), to make a distinction between movement and non-movement. We used the acquisition parameters (320 × 240 at 12.5 fps), derived from a previous study (Cuppens et al., Proceedings of the 4th European congress of the international federation for medical and biological engineering (MBEC 2008), ECIFBME 2008, Antwerp, Belgium, IFMBE Proceedings, vol 22, pp 784–789, 2008). Two experiments were concluded, one with global thresholds of R(t) in all datasets and one with a variable threshold in each dataset. The latter is obtained by inspecting a non-movement epoch and calculating the mean and standard deviations of R(t) over time. The variable threshold on R(t) was then obtained for each dataset by adding to the mean a fixed multiple of the standard deviation. Optimal thresholds were derived based on a three-fold cross-validation. The best result was achieved when using a variable threshold, which resulted in a sensitivity of one in all the test sets and a PPV of 1, 0.821, and 1, respectively, for the three test sets. Epilepsy Pediatric patients Optical flow Movement detection Nocturnal recordings Lagae, Lieven aut Ceulemans, Berten aut Van Huffel, Sabine aut Vanrumste, Bart aut Enthalten in Medical & biological engineering & computing Springer-Verlag, 1977 48(2010), 9 vom: 24. Juni, Seite 923-931 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:48 year:2010 number:9 day:24 month:06 pages:923-931 https://doi.org/10.1007/s11517-010-0648-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_32 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 48 2010 9 24 06 923-931 |
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10.1007/s11517-010-0648-4 doi (DE-627)OLC2038687366 (DE-He213)s11517-010-0648-4-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Cuppens, Kris verfasserin aut Automatic video detection of body movement during sleep based on optical flow in pediatric patients with epilepsy 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2010 Abstract The aim of our work is to investigate whether the optical flow algorithm applied to video recordings can be used to detect movement during sleep in pediatric patients with epilepsy. The optical flow algorithm allocates intensities to pixels proportional to their involvement in movement of an object. The average of a percentage of the highest movement vectors was plotted as a function of time (R(t)). The used dataset contains video data acquired at the University Hospital of Leuven consisting of normal sleep movement and seizure movement. We investigated R(t), to make a distinction between movement and non-movement. We used the acquisition parameters (320 × 240 at 12.5 fps), derived from a previous study (Cuppens et al., Proceedings of the 4th European congress of the international federation for medical and biological engineering (MBEC 2008), ECIFBME 2008, Antwerp, Belgium, IFMBE Proceedings, vol 22, pp 784–789, 2008). Two experiments were concluded, one with global thresholds of R(t) in all datasets and one with a variable threshold in each dataset. The latter is obtained by inspecting a non-movement epoch and calculating the mean and standard deviations of R(t) over time. The variable threshold on R(t) was then obtained for each dataset by adding to the mean a fixed multiple of the standard deviation. Optimal thresholds were derived based on a three-fold cross-validation. The best result was achieved when using a variable threshold, which resulted in a sensitivity of one in all the test sets and a PPV of 1, 0.821, and 1, respectively, for the three test sets. Epilepsy Pediatric patients Optical flow Movement detection Nocturnal recordings Lagae, Lieven aut Ceulemans, Berten aut Van Huffel, Sabine aut Vanrumste, Bart aut Enthalten in Medical & biological engineering & computing Springer-Verlag, 1977 48(2010), 9 vom: 24. Juni, Seite 923-931 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:48 year:2010 number:9 day:24 month:06 pages:923-931 https://doi.org/10.1007/s11517-010-0648-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_32 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 48 2010 9 24 06 923-931 |
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10.1007/s11517-010-0648-4 doi (DE-627)OLC2038687366 (DE-He213)s11517-010-0648-4-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Cuppens, Kris verfasserin aut Automatic video detection of body movement during sleep based on optical flow in pediatric patients with epilepsy 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2010 Abstract The aim of our work is to investigate whether the optical flow algorithm applied to video recordings can be used to detect movement during sleep in pediatric patients with epilepsy. The optical flow algorithm allocates intensities to pixels proportional to their involvement in movement of an object. The average of a percentage of the highest movement vectors was plotted as a function of time (R(t)). The used dataset contains video data acquired at the University Hospital of Leuven consisting of normal sleep movement and seizure movement. We investigated R(t), to make a distinction between movement and non-movement. We used the acquisition parameters (320 × 240 at 12.5 fps), derived from a previous study (Cuppens et al., Proceedings of the 4th European congress of the international federation for medical and biological engineering (MBEC 2008), ECIFBME 2008, Antwerp, Belgium, IFMBE Proceedings, vol 22, pp 784–789, 2008). Two experiments were concluded, one with global thresholds of R(t) in all datasets and one with a variable threshold in each dataset. The latter is obtained by inspecting a non-movement epoch and calculating the mean and standard deviations of R(t) over time. The variable threshold on R(t) was then obtained for each dataset by adding to the mean a fixed multiple of the standard deviation. Optimal thresholds were derived based on a three-fold cross-validation. The best result was achieved when using a variable threshold, which resulted in a sensitivity of one in all the test sets and a PPV of 1, 0.821, and 1, respectively, for the three test sets. Epilepsy Pediatric patients Optical flow Movement detection Nocturnal recordings Lagae, Lieven aut Ceulemans, Berten aut Van Huffel, Sabine aut Vanrumste, Bart aut Enthalten in Medical & biological engineering & computing Springer-Verlag, 1977 48(2010), 9 vom: 24. Juni, Seite 923-931 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:48 year:2010 number:9 day:24 month:06 pages:923-931 https://doi.org/10.1007/s11517-010-0648-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_32 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 48 2010 9 24 06 923-931 |
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10.1007/s11517-010-0648-4 doi (DE-627)OLC2038687366 (DE-He213)s11517-010-0648-4-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Cuppens, Kris verfasserin aut Automatic video detection of body movement during sleep based on optical flow in pediatric patients with epilepsy 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2010 Abstract The aim of our work is to investigate whether the optical flow algorithm applied to video recordings can be used to detect movement during sleep in pediatric patients with epilepsy. The optical flow algorithm allocates intensities to pixels proportional to their involvement in movement of an object. The average of a percentage of the highest movement vectors was plotted as a function of time (R(t)). The used dataset contains video data acquired at the University Hospital of Leuven consisting of normal sleep movement and seizure movement. We investigated R(t), to make a distinction between movement and non-movement. We used the acquisition parameters (320 × 240 at 12.5 fps), derived from a previous study (Cuppens et al., Proceedings of the 4th European congress of the international federation for medical and biological engineering (MBEC 2008), ECIFBME 2008, Antwerp, Belgium, IFMBE Proceedings, vol 22, pp 784–789, 2008). Two experiments were concluded, one with global thresholds of R(t) in all datasets and one with a variable threshold in each dataset. The latter is obtained by inspecting a non-movement epoch and calculating the mean and standard deviations of R(t) over time. The variable threshold on R(t) was then obtained for each dataset by adding to the mean a fixed multiple of the standard deviation. Optimal thresholds were derived based on a three-fold cross-validation. The best result was achieved when using a variable threshold, which resulted in a sensitivity of one in all the test sets and a PPV of 1, 0.821, and 1, respectively, for the three test sets. Epilepsy Pediatric patients Optical flow Movement detection Nocturnal recordings Lagae, Lieven aut Ceulemans, Berten aut Van Huffel, Sabine aut Vanrumste, Bart aut Enthalten in Medical & biological engineering & computing Springer-Verlag, 1977 48(2010), 9 vom: 24. Juni, Seite 923-931 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:48 year:2010 number:9 day:24 month:06 pages:923-931 https://doi.org/10.1007/s11517-010-0648-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_32 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 48 2010 9 24 06 923-931 |
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10.1007/s11517-010-0648-4 doi (DE-627)OLC2038687366 (DE-He213)s11517-010-0648-4-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Cuppens, Kris verfasserin aut Automatic video detection of body movement during sleep based on optical flow in pediatric patients with epilepsy 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2010 Abstract The aim of our work is to investigate whether the optical flow algorithm applied to video recordings can be used to detect movement during sleep in pediatric patients with epilepsy. The optical flow algorithm allocates intensities to pixels proportional to their involvement in movement of an object. The average of a percentage of the highest movement vectors was plotted as a function of time (R(t)). The used dataset contains video data acquired at the University Hospital of Leuven consisting of normal sleep movement and seizure movement. We investigated R(t), to make a distinction between movement and non-movement. We used the acquisition parameters (320 × 240 at 12.5 fps), derived from a previous study (Cuppens et al., Proceedings of the 4th European congress of the international federation for medical and biological engineering (MBEC 2008), ECIFBME 2008, Antwerp, Belgium, IFMBE Proceedings, vol 22, pp 784–789, 2008). Two experiments were concluded, one with global thresholds of R(t) in all datasets and one with a variable threshold in each dataset. The latter is obtained by inspecting a non-movement epoch and calculating the mean and standard deviations of R(t) over time. The variable threshold on R(t) was then obtained for each dataset by adding to the mean a fixed multiple of the standard deviation. Optimal thresholds were derived based on a three-fold cross-validation. The best result was achieved when using a variable threshold, which resulted in a sensitivity of one in all the test sets and a PPV of 1, 0.821, and 1, respectively, for the three test sets. Epilepsy Pediatric patients Optical flow Movement detection Nocturnal recordings Lagae, Lieven aut Ceulemans, Berten aut Van Huffel, Sabine aut Vanrumste, Bart aut Enthalten in Medical & biological engineering & computing Springer-Verlag, 1977 48(2010), 9 vom: 24. Juni, Seite 923-931 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:48 year:2010 number:9 day:24 month:06 pages:923-931 https://doi.org/10.1007/s11517-010-0648-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_32 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4219 AR 48 2010 9 24 06 923-931 |
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automatic video detection of body movement during sleep based on optical flow in pediatric patients with epilepsy |
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Automatic video detection of body movement during sleep based on optical flow in pediatric patients with epilepsy |
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Abstract The aim of our work is to investigate whether the optical flow algorithm applied to video recordings can be used to detect movement during sleep in pediatric patients with epilepsy. The optical flow algorithm allocates intensities to pixels proportional to their involvement in movement of an object. The average of a percentage of the highest movement vectors was plotted as a function of time (R(t)). The used dataset contains video data acquired at the University Hospital of Leuven consisting of normal sleep movement and seizure movement. We investigated R(t), to make a distinction between movement and non-movement. We used the acquisition parameters (320 × 240 at 12.5 fps), derived from a previous study (Cuppens et al., Proceedings of the 4th European congress of the international federation for medical and biological engineering (MBEC 2008), ECIFBME 2008, Antwerp, Belgium, IFMBE Proceedings, vol 22, pp 784–789, 2008). Two experiments were concluded, one with global thresholds of R(t) in all datasets and one with a variable threshold in each dataset. The latter is obtained by inspecting a non-movement epoch and calculating the mean and standard deviations of R(t) over time. The variable threshold on R(t) was then obtained for each dataset by adding to the mean a fixed multiple of the standard deviation. Optimal thresholds were derived based on a three-fold cross-validation. The best result was achieved when using a variable threshold, which resulted in a sensitivity of one in all the test sets and a PPV of 1, 0.821, and 1, respectively, for the three test sets. © International Federation for Medical and Biological Engineering 2010 |
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Abstract The aim of our work is to investigate whether the optical flow algorithm applied to video recordings can be used to detect movement during sleep in pediatric patients with epilepsy. The optical flow algorithm allocates intensities to pixels proportional to their involvement in movement of an object. The average of a percentage of the highest movement vectors was plotted as a function of time (R(t)). The used dataset contains video data acquired at the University Hospital of Leuven consisting of normal sleep movement and seizure movement. We investigated R(t), to make a distinction between movement and non-movement. We used the acquisition parameters (320 × 240 at 12.5 fps), derived from a previous study (Cuppens et al., Proceedings of the 4th European congress of the international federation for medical and biological engineering (MBEC 2008), ECIFBME 2008, Antwerp, Belgium, IFMBE Proceedings, vol 22, pp 784–789, 2008). Two experiments were concluded, one with global thresholds of R(t) in all datasets and one with a variable threshold in each dataset. The latter is obtained by inspecting a non-movement epoch and calculating the mean and standard deviations of R(t) over time. The variable threshold on R(t) was then obtained for each dataset by adding to the mean a fixed multiple of the standard deviation. Optimal thresholds were derived based on a three-fold cross-validation. The best result was achieved when using a variable threshold, which resulted in a sensitivity of one in all the test sets and a PPV of 1, 0.821, and 1, respectively, for the three test sets. © International Federation for Medical and Biological Engineering 2010 |
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Abstract The aim of our work is to investigate whether the optical flow algorithm applied to video recordings can be used to detect movement during sleep in pediatric patients with epilepsy. The optical flow algorithm allocates intensities to pixels proportional to their involvement in movement of an object. The average of a percentage of the highest movement vectors was plotted as a function of time (R(t)). The used dataset contains video data acquired at the University Hospital of Leuven consisting of normal sleep movement and seizure movement. We investigated R(t), to make a distinction between movement and non-movement. We used the acquisition parameters (320 × 240 at 12.5 fps), derived from a previous study (Cuppens et al., Proceedings of the 4th European congress of the international federation for medical and biological engineering (MBEC 2008), ECIFBME 2008, Antwerp, Belgium, IFMBE Proceedings, vol 22, pp 784–789, 2008). Two experiments were concluded, one with global thresholds of R(t) in all datasets and one with a variable threshold in each dataset. The latter is obtained by inspecting a non-movement epoch and calculating the mean and standard deviations of R(t) over time. The variable threshold on R(t) was then obtained for each dataset by adding to the mean a fixed multiple of the standard deviation. Optimal thresholds were derived based on a three-fold cross-validation. The best result was achieved when using a variable threshold, which resulted in a sensitivity of one in all the test sets and a PPV of 1, 0.821, and 1, respectively, for the three test sets. © International Federation for Medical and Biological Engineering 2010 |
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