Online apnea–bradycardia detection based on hidden semi-Markov models
Abstract In this paper, we propose a new online apnea–bradycardia detection scheme that takes into account not only the instantaneous values of time series, but also their temporal evolution. The detector is based on a set of hidden semi-Markov models, representing the temporal evolution of beat-to-...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Altuve, Miguel [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Anmerkung: |
© International Federation for Medical and Biological Engineering 2014 |
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| Übergeordnetes Werk: |
Enthalten in: Medical & biological engineering & computing - Springer Berlin Heidelberg, 1977, 53(2014), 1 vom: 10. Okt., Seite 1-13 |
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| Übergeordnetes Werk: |
volume:53 ; year:2014 ; number:1 ; day:10 ; month:10 ; pages:1-13 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11517-014-1207-1 |
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OLC2038692793 |
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| 520 | |a Abstract In this paper, we propose a new online apnea–bradycardia detection scheme that takes into account not only the instantaneous values of time series, but also their temporal evolution. The detector is based on a set of hidden semi-Markov models, representing the temporal evolution of beat-to-beat interval (RR interval) time series. A preprocessing step, including quantization and delayed version of the observation vector, is also proposed to maximize detection performance. This approach is quantitatively evaluated through simulated and real signals, the latter being acquired in neonatal intensive care units (NICU). Compared to two conventional detectors used in NICU, our best detector shows an improvement on average of around 15 % in sensitivity and 7 % in specificity. Furthermore, a reduced detection delay of approximately 2 s is also observed with respect to conventional detectors. | ||
| 650 | 4 | |a Time series analysis | |
| 650 | 4 | |a Hidden semi-Markov models | |
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| 700 | 1 | |a Carrault, Guy |4 aut | |
| 700 | 1 | |a Beuchée, Alain |4 aut | |
| 700 | 1 | |a Pladys, Patrick |4 aut | |
| 700 | 1 | |a Hernández, Alfredo I. |4 aut | |
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10.1007/s11517-014-1207-1 doi (DE-627)OLC2038692793 (DE-He213)s11517-014-1207-1-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Altuve, Miguel verfasserin aut Online apnea–bradycardia detection based on hidden semi-Markov models 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2014 Abstract In this paper, we propose a new online apnea–bradycardia detection scheme that takes into account not only the instantaneous values of time series, but also their temporal evolution. The detector is based on a set of hidden semi-Markov models, representing the temporal evolution of beat-to-beat interval (RR interval) time series. A preprocessing step, including quantization and delayed version of the observation vector, is also proposed to maximize detection performance. This approach is quantitatively evaluated through simulated and real signals, the latter being acquired in neonatal intensive care units (NICU). Compared to two conventional detectors used in NICU, our best detector shows an improvement on average of around 15 % in sensitivity and 7 % in specificity. Furthermore, a reduced detection delay of approximately 2 s is also observed with respect to conventional detectors. Time series analysis Hidden semi-Markov models Data mining Electrocardiogram Apnea–bradycardia Carrault, Guy aut Beuchée, Alain aut Pladys, Patrick aut Hernández, Alfredo I. aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 53(2014), 1 vom: 10. Okt., Seite 1-13 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:53 year:2014 number:1 day:10 month:10 pages:1-13 https://doi.org/10.1007/s11517-014-1207-1 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_2018 GBV_ILN_4012 GBV_ILN_4219 AR 53 2014 1 10 10 1-13 |
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10.1007/s11517-014-1207-1 doi (DE-627)OLC2038692793 (DE-He213)s11517-014-1207-1-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Altuve, Miguel verfasserin aut Online apnea–bradycardia detection based on hidden semi-Markov models 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2014 Abstract In this paper, we propose a new online apnea–bradycardia detection scheme that takes into account not only the instantaneous values of time series, but also their temporal evolution. The detector is based on a set of hidden semi-Markov models, representing the temporal evolution of beat-to-beat interval (RR interval) time series. A preprocessing step, including quantization and delayed version of the observation vector, is also proposed to maximize detection performance. This approach is quantitatively evaluated through simulated and real signals, the latter being acquired in neonatal intensive care units (NICU). Compared to two conventional detectors used in NICU, our best detector shows an improvement on average of around 15 % in sensitivity and 7 % in specificity. Furthermore, a reduced detection delay of approximately 2 s is also observed with respect to conventional detectors. Time series analysis Hidden semi-Markov models Data mining Electrocardiogram Apnea–bradycardia Carrault, Guy aut Beuchée, Alain aut Pladys, Patrick aut Hernández, Alfredo I. aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 53(2014), 1 vom: 10. Okt., Seite 1-13 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:53 year:2014 number:1 day:10 month:10 pages:1-13 https://doi.org/10.1007/s11517-014-1207-1 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_2018 GBV_ILN_4012 GBV_ILN_4219 AR 53 2014 1 10 10 1-13 |
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10.1007/s11517-014-1207-1 doi (DE-627)OLC2038692793 (DE-He213)s11517-014-1207-1-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Altuve, Miguel verfasserin aut Online apnea–bradycardia detection based on hidden semi-Markov models 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2014 Abstract In this paper, we propose a new online apnea–bradycardia detection scheme that takes into account not only the instantaneous values of time series, but also their temporal evolution. The detector is based on a set of hidden semi-Markov models, representing the temporal evolution of beat-to-beat interval (RR interval) time series. A preprocessing step, including quantization and delayed version of the observation vector, is also proposed to maximize detection performance. This approach is quantitatively evaluated through simulated and real signals, the latter being acquired in neonatal intensive care units (NICU). Compared to two conventional detectors used in NICU, our best detector shows an improvement on average of around 15 % in sensitivity and 7 % in specificity. Furthermore, a reduced detection delay of approximately 2 s is also observed with respect to conventional detectors. Time series analysis Hidden semi-Markov models Data mining Electrocardiogram Apnea–bradycardia Carrault, Guy aut Beuchée, Alain aut Pladys, Patrick aut Hernández, Alfredo I. aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 53(2014), 1 vom: 10. Okt., Seite 1-13 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:53 year:2014 number:1 day:10 month:10 pages:1-13 https://doi.org/10.1007/s11517-014-1207-1 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_2018 GBV_ILN_4012 GBV_ILN_4219 AR 53 2014 1 10 10 1-13 |
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10.1007/s11517-014-1207-1 doi (DE-627)OLC2038692793 (DE-He213)s11517-014-1207-1-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Altuve, Miguel verfasserin aut Online apnea–bradycardia detection based on hidden semi-Markov models 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Federation for Medical and Biological Engineering 2014 Abstract In this paper, we propose a new online apnea–bradycardia detection scheme that takes into account not only the instantaneous values of time series, but also their temporal evolution. The detector is based on a set of hidden semi-Markov models, representing the temporal evolution of beat-to-beat interval (RR interval) time series. A preprocessing step, including quantization and delayed version of the observation vector, is also proposed to maximize detection performance. This approach is quantitatively evaluated through simulated and real signals, the latter being acquired in neonatal intensive care units (NICU). Compared to two conventional detectors used in NICU, our best detector shows an improvement on average of around 15 % in sensitivity and 7 % in specificity. Furthermore, a reduced detection delay of approximately 2 s is also observed with respect to conventional detectors. Time series analysis Hidden semi-Markov models Data mining Electrocardiogram Apnea–bradycardia Carrault, Guy aut Beuchée, Alain aut Pladys, Patrick aut Hernández, Alfredo I. aut Enthalten in Medical & biological engineering & computing Springer Berlin Heidelberg, 1977 53(2014), 1 vom: 10. Okt., Seite 1-13 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:53 year:2014 number:1 day:10 month:10 pages:1-13 https://doi.org/10.1007/s11517-014-1207-1 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_2018 GBV_ILN_4012 GBV_ILN_4219 AR 53 2014 1 10 10 1-13 |
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Online apnea–bradycardia detection based on hidden semi-Markov models |
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Abstract In this paper, we propose a new online apnea–bradycardia detection scheme that takes into account not only the instantaneous values of time series, but also their temporal evolution. The detector is based on a set of hidden semi-Markov models, representing the temporal evolution of beat-to-beat interval (RR interval) time series. A preprocessing step, including quantization and delayed version of the observation vector, is also proposed to maximize detection performance. This approach is quantitatively evaluated through simulated and real signals, the latter being acquired in neonatal intensive care units (NICU). Compared to two conventional detectors used in NICU, our best detector shows an improvement on average of around 15 % in sensitivity and 7 % in specificity. Furthermore, a reduced detection delay of approximately 2 s is also observed with respect to conventional detectors. © International Federation for Medical and Biological Engineering 2014 |
| abstractGer |
Abstract In this paper, we propose a new online apnea–bradycardia detection scheme that takes into account not only the instantaneous values of time series, but also their temporal evolution. The detector is based on a set of hidden semi-Markov models, representing the temporal evolution of beat-to-beat interval (RR interval) time series. A preprocessing step, including quantization and delayed version of the observation vector, is also proposed to maximize detection performance. This approach is quantitatively evaluated through simulated and real signals, the latter being acquired in neonatal intensive care units (NICU). Compared to two conventional detectors used in NICU, our best detector shows an improvement on average of around 15 % in sensitivity and 7 % in specificity. Furthermore, a reduced detection delay of approximately 2 s is also observed with respect to conventional detectors. © International Federation for Medical and Biological Engineering 2014 |
| abstract_unstemmed |
Abstract In this paper, we propose a new online apnea–bradycardia detection scheme that takes into account not only the instantaneous values of time series, but also their temporal evolution. The detector is based on a set of hidden semi-Markov models, representing the temporal evolution of beat-to-beat interval (RR interval) time series. A preprocessing step, including quantization and delayed version of the observation vector, is also proposed to maximize detection performance. This approach is quantitatively evaluated through simulated and real signals, the latter being acquired in neonatal intensive care units (NICU). Compared to two conventional detectors used in NICU, our best detector shows an improvement on average of around 15 % in sensitivity and 7 % in specificity. Furthermore, a reduced detection delay of approximately 2 s is also observed with respect to conventional detectors. © International Federation for Medical and Biological Engineering 2014 |
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| title_short |
Online apnea–bradycardia detection based on hidden semi-Markov models |
| url |
https://doi.org/10.1007/s11517-014-1207-1 |
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false |
| author2 |
Carrault, Guy Beuchée, Alain Pladys, Patrick Hernández, Alfredo I. |
| author2Str |
Carrault, Guy Beuchée, Alain Pladys, Patrick Hernández, Alfredo I. |
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129858552 |
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| doi_str |
10.1007/s11517-014-1207-1 |
| up_date |
2024-07-03T19:53:34.856Z |
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7.400016 |