Prediction of pharmacologically induced baroreflex sensitivity from local time and frequency domain indices of R-R interval and systolic blood pressure signals obtained during deep breathing
| Autor: | Sami Arica, Abdi Bozkurt, Ahmet Birand, Ahmed H. Tewfik, N. Firat Ince |
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| Přispěvatelé: | Çukurova Üniversitesi |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Adult
Male medicine.medical_specialty Correlation coefficient Heart rate Diaphragmatic breathing Health Informatics AR modeling Baroreflex Multivariate linear prediction Models Biological Cross-validation Electrocardiography Phenylephrine Internal medicine Statistics medicine Humans Vasoconstrictor Agents Time domain business.industry Respiration fungi Reproducibility of Results Signal Processing Computer-Assisted Middle Aged Computer Science Applications R-R Interval Blood pressure Frequency domain Baroreflex sensitivity Multivariate Analysis Cardiology Female Kalman filter business Algorithms |
| Popis: | PubMedID: 21550604 Pharmacological measurement of baroreflex sensitivity (BRS) is widely accepted and used in clinical practice. Following the introduction of pharmacologically induced BRS (p-BRS), alternative assessment methods eliminating the use of drugs were in the center of interest of the cardiovascular research community. In this study we investigated whether p-BRS using phenylephrine injection can be predicted from non-pharmacological time and frequency domain indices computed from electrocardiogram (ECG) and blood pressure (BP) data acquired during deep breathing. In this scheme, ECG and BP data were recorded from 16 subjects in a two-phase experiment. In the first phase the subjects performed irregular deep breaths and in the second phase the subjects received phenylephrine injection. From the first phase of the experiment, a large pool of predictors describing the local characteristic of beat-to-beat interval tachogram (RR) and systolic blood pressure (SBP) were extracted in time and frequency domains. A subset of these indices was selected using twelve subjects with an exhaustive search fused with a leave one subject out cross validation procedure. The selected indices were used to predict the p-BRS on the remaining four test subjects. A multivariate regression was used in all prediction steps. The algorithm achieved best prediction accuracy with only two features extracted from the deep breathing data, one from the frequency and the other from the time domain. The normalized L2-norm error was computed as 22.9% and the correlation coefficient was 0.97 (p=0.03). These results suggest that the p-BRS can be estimated from non-pharmacological indices computed from ECG and invasive BP data related to deep breathing. © 2011 Elsevier Ltd. This study was supported by The Scientific and Technological Research Council of Turkey (TÜBİTAK). |
| Databáze: | OpenAIRE |
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