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136 results on '"Muehlsteff, J."'

1. Using MRI to gain insights on cuff-based physiological measurements

Author

Bogatu, L, Hoppenbrouwers, J, van den Bosch, H, Turco, S, Mischi, M, Schmitt, L, Woerlee, P, Bouwman, A, Korsten, HMH, Muehlsteff, J, Bogatu, L, Hoppenbrouwers, J, van den Bosch, H, Turco, S, Mischi, M, Schmitt, L, Woerlee, P, Bouwman, A, Korsten, HMH, and Muehlsteff, J

Published
2023

5. MRI as research tool for cuff‑based physiological measurements

Author

Bogatu, Laura, Hoppenbrouwers, J., van den Bosch, H., Turco, S., Mischi, M., Muehlsteff, J., Schmitt, L., Woerlee, P.H., Korsten, H., Bouwman, R.A., Bogatu, Laura, Hoppenbrouwers, J., van den Bosch, H., Turco, S., Mischi, M., Muehlsteff, J., Schmitt, L., Woerlee, P.H., Korsten, H., and Bouwman, R.A.

Published
2022

11. Systolic blood pressure estimation using PPG during physical exercise

Author

Sun, S., Bezemer, R., Long, X., Muehlsteff, J., Aarts, R.M., Signal Processing Systems, and Biomedical Diagnostics Lab

Subjects
Blood Pressure, photoplethysmography
Abstract

A model to estimate systolic blood pressure (SBP) using finger-derived photoplethysmography (PPG) signals is proposed for 19 healthy subjects during physical exercise. The estimation was done for every 30 s during the period of exercise, before which an initialization process for each subject at rest was performed. Eighteen features were extracted from PPG signals and a multiple linear regression (MLR) method was employed to combine all derived features to estimate SBP. Our results showed that the proposed model had a performance in estimating SBP with a mean difference of -0.40 mmHg, a standard deviation of difference of 14.07 mmHg, and a median intra-subject correlation coefficients of 0.85. The present work demonstrated promising results of estimating SBP using PPG during physical exercise.

Published
2016

15. Systolic blood pressure estimation using PPG and ECG during physical exercise

Author

Sun, S., Bezemer, R., Long, X., Muehlsteff, J., Aarts, R.M., Sun, S., Bezemer, R., Long, X., Muehlsteff, J., and Aarts, R.M.

Abstract

In this work, a model to estimate systolic blood pressure (SBP) using photoplethysmography (PPG) and electrocardiography (ECG) is proposed. Data from 19 subjects doing a 40 min exercise was analyzed. Reference SBP was measured at the finger based on the volume-clamp principle. PPG signals were measured at the finger and forehead. After an initialization process for each subject at rest, the model estimated SBP every 30 s for the whole period of exercise. In order to build this model, 18 features were extracted from PPG signals by means of its waveform, first derivative, second derivative, and frequency spectrum. In addition, pulse arrival time (PAT) was derived as a feature from the combination of PPG and ECG. After evaluating four regression models, we chose multiple linear regression (MLR) to combine all derived features to estimate SBP. The contribution of each feature was quantified using its normalized weight in the MLR. To evaluate the performance of the model, we used a leave-one-subject-out cross validation. With the aim of exploring the potential of the model, we investigated the influences of the inclusion of PAT, regression models, measurement sites (finger and forehead), and posture change (lying, sitting, and standing). The results show that the inclusion of PAT reduced the standard deviation (SD) of the difference from 14.07 to 13.52 mmHg. There was no significant difference in the estimation performance between the model using finger- and forehead-derived PPG signals. Separate models are necessary for different postures. The optimized model using finger-derived PPG signals during physical exercise had a performance with a mean difference of 0.43 mmHg, an SD of difference of 13.52 mmHg, and median correlation coefficients of 0.86. Furthermore, we identified two groups of features that contributed more to SBP estimation compared to other features. One group consists of our proposed features depicting beat morphology. The other comprises existing feature

Published
2016

17. Can PPG be used for HRV analysis?

Author

Pinheiro, N., primary, Couceiro, R., additional, Henriques, J., additional, Muehlsteff, J., additional, Quintal, I., additional, Goncalves, L., additional, and Carvalho, P., additional

Published
2016
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18. Real-Time Prediction of Neurally Mediated Syncope

Author

Couceiro, R., primary, Carvalho, P., additional, Paiva, R. P., additional, Muehlsteff, J., additional, Henriques, J., additional, Eickholt, C., additional, Brinkmeyer, C., additional, Kelm, M., additional, and Meyer, C., additional

Published
2016
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