An Innovative Technique to Assess Spontaneous Baroreflex Sensitivity with Short Data Segments: Multiple Trigonometric Regressive Spectral Analysis
Objective: As the multiple trigonometric regressive spectral (MTRS) analysis is extraordinary in its ability to analyze short local data segments down to 12 s, we wanted to evaluate the impact of the data segment settings by applying the technique of MTRS analysis for baroreflex sensitivity (BRS) es...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-01-01
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| Series: | Frontiers in Physiology |
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| Online Access: | http://journal.frontiersin.org/article/10.3389/fphys.2018.00010/full |
| _version_ | 1828339915262263296 |
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| author | Kai Li Kai Li Heinz Rüdiger Rocco Haase Tjalf Ziemssen |
| author_facet | Kai Li Kai Li Heinz Rüdiger Rocco Haase Tjalf Ziemssen |
| author_sort | Kai Li |
| collection | DOAJ |
| description | Objective: As the multiple trigonometric regressive spectral (MTRS) analysis is extraordinary in its ability to analyze short local data segments down to 12 s, we wanted to evaluate the impact of the data segment settings by applying the technique of MTRS analysis for baroreflex sensitivity (BRS) estimation using a standardized data pool.Methods: Spectral and baroreflex analyses were performed on the EuroBaVar dataset (42 recordings, including lying and standing positions). For this analysis, the technique of MTRS was used. We used different global and local data segment lengths, and chose the global data segments from different positions. Three global data segments of 1 and 2 min and three local data segments of 12, 20, and 30 s were used in MTRS analysis for BRS.Results: All the BRS-values calculated on the three global data segments were highly correlated, both in the supine and standing positions; the different global data segments provided similar BRS estimations. When using different local data segments, all the BRS-values were also highly correlated. However, in the supine position, using short local data segments of 12 s overestimated BRS compared with those using 20 and 30 s. In the standing position, the BRS estimations using different local data segments were comparable. There was no proportional bias for the comparisons between different BRS estimations.Conclusion: We demonstrate that BRS estimation by the MTRS technique is stable when using different global data segments, and MTRS is extraordinary in its ability to evaluate BRS in even short local data segments (20 and 30 s). Because of the non-stationary character of most biosignals, the MTRS technique would be preferable for BRS analysis especially in conditions when only short stationary data segments are available or when dynamic changes of BRS should be monitored. |
| first_indexed | 2024-04-13T22:48:46Z |
| format | Article |
| id | doaj.art-e084d276df304d7da603e542452fbe32 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-13T22:48:46Z |
| publishDate | 2018-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-e084d276df304d7da603e542452fbe322022-12-22T02:26:17ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2018-01-01910.3389/fphys.2018.00010313726An Innovative Technique to Assess Spontaneous Baroreflex Sensitivity with Short Data Segments: Multiple Trigonometric Regressive Spectral AnalysisKai Li0Kai Li1Heinz Rüdiger2Rocco Haase3Tjalf Ziemssen4Autonomic and Neuroendocrinological Lab, Department of Neurology, Center of Clinical Neuroscience, University Hospital Carl-Gustav Carus, Technical University of Dresden, Dresden, GermanyDepartment of Neurology, Beijing Hospital, National Center of Gerontology, Beijing, ChinaAutonomic and Neuroendocrinological Lab, Department of Neurology, Center of Clinical Neuroscience, University Hospital Carl-Gustav Carus, Technical University of Dresden, Dresden, GermanyAutonomic and Neuroendocrinological Lab, Department of Neurology, Center of Clinical Neuroscience, University Hospital Carl-Gustav Carus, Technical University of Dresden, Dresden, GermanyAutonomic and Neuroendocrinological Lab, Department of Neurology, Center of Clinical Neuroscience, University Hospital Carl-Gustav Carus, Technical University of Dresden, Dresden, GermanyObjective: As the multiple trigonometric regressive spectral (MTRS) analysis is extraordinary in its ability to analyze short local data segments down to 12 s, we wanted to evaluate the impact of the data segment settings by applying the technique of MTRS analysis for baroreflex sensitivity (BRS) estimation using a standardized data pool.Methods: Spectral and baroreflex analyses were performed on the EuroBaVar dataset (42 recordings, including lying and standing positions). For this analysis, the technique of MTRS was used. We used different global and local data segment lengths, and chose the global data segments from different positions. Three global data segments of 1 and 2 min and three local data segments of 12, 20, and 30 s were used in MTRS analysis for BRS.Results: All the BRS-values calculated on the three global data segments were highly correlated, both in the supine and standing positions; the different global data segments provided similar BRS estimations. When using different local data segments, all the BRS-values were also highly correlated. However, in the supine position, using short local data segments of 12 s overestimated BRS compared with those using 20 and 30 s. In the standing position, the BRS estimations using different local data segments were comparable. There was no proportional bias for the comparisons between different BRS estimations.Conclusion: We demonstrate that BRS estimation by the MTRS technique is stable when using different global data segments, and MTRS is extraordinary in its ability to evaluate BRS in even short local data segments (20 and 30 s). Because of the non-stationary character of most biosignals, the MTRS technique would be preferable for BRS analysis especially in conditions when only short stationary data segments are available or when dynamic changes of BRS should be monitored.http://journal.frontiersin.org/article/10.3389/fphys.2018.00010/fullbaroreflex sensitivitymultiple trigonometric regressive spectral analysisbaroreflex functiondata segmentautonomic nervous system |
| spellingShingle | Kai Li Kai Li Heinz Rüdiger Rocco Haase Tjalf Ziemssen An Innovative Technique to Assess Spontaneous Baroreflex Sensitivity with Short Data Segments: Multiple Trigonometric Regressive Spectral Analysis Frontiers in Physiology baroreflex sensitivity multiple trigonometric regressive spectral analysis baroreflex function data segment autonomic nervous system |
| title | An Innovative Technique to Assess Spontaneous Baroreflex Sensitivity with Short Data Segments: Multiple Trigonometric Regressive Spectral Analysis |
| title_full | An Innovative Technique to Assess Spontaneous Baroreflex Sensitivity with Short Data Segments: Multiple Trigonometric Regressive Spectral Analysis |
| title_fullStr | An Innovative Technique to Assess Spontaneous Baroreflex Sensitivity with Short Data Segments: Multiple Trigonometric Regressive Spectral Analysis |
| title_full_unstemmed | An Innovative Technique to Assess Spontaneous Baroreflex Sensitivity with Short Data Segments: Multiple Trigonometric Regressive Spectral Analysis |
| title_short | An Innovative Technique to Assess Spontaneous Baroreflex Sensitivity with Short Data Segments: Multiple Trigonometric Regressive Spectral Analysis |
| title_sort | innovative technique to assess spontaneous baroreflex sensitivity with short data segments multiple trigonometric regressive spectral analysis |
| topic | baroreflex sensitivity multiple trigonometric regressive spectral analysis baroreflex function data segment autonomic nervous system |
| url | http://journal.frontiersin.org/article/10.3389/fphys.2018.00010/full |
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