| 1. |
- Anderson, Rachele, et al.
(författare)
-
Effects of Age, BMI, anxiety and stress on the parameters of a stochastic model for heart rate variability including respiratory information
- 2018
-
Ingår i: BIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018. - : SCITEPRESS - Science and Technology Publications. - 9789897582790 ; 4, s. 17-25
-
Konferensbidrag (refereegranskat)abstract
- Recent studies have focused on investigating different factors that may affect heart rate variability (HRV), pointing especially to the effects of age, gender and stress level. Other findings raise the importance of considering the respiratory frequency in the analysis of HRV signals. In this study, we evaluate the effect of several covariates on the parameters of a stochastic model for HRV. The data was recorded from 47 test participants, whose breathing was controlled by following a metronome with increasing frequency. This setup allows for a controlled acquisition of respiratory related HRV data covering the frequency range in which adults breathe in different everyday situations. A stochastic model, known as Locally Stationary Chirp Process, accounts for the respiratory signal information and models the HRV data. The model parameters are estimated with a novel inference method based on the separability features possessed by the process covariance function. Least square regression analysis using several available covariates is used to investigate the correlation with the stochastic model parameters. The results show statistically significant correlation of the model parameters with age, BMI, State and Trait Anxiety as well as stress level.
|
|
| 2. |
- Anderson, Rachele, et al.
(författare)
-
Insights on Spectral Measures for HRV Based on a Novel Approach for Data Acquisition
- 2018
-
Ingår i: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. - 1557-170X. ; 2018, s. 510-513
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present new insights on classical spectral measures for heart rate variability (HRV), based on a novel method for HRV acquisition. A dynamic breathing task, where the test participants are asked to breathe following a metronome with slowly increasing frequency, allows for the acquisition of respiratory-related HRV-data covering the frequency range in which adults breathe in different everyday situations. We discuss how the use of a time-frequency representation, e.g. the spectrogram or the Wigner-Ville distribution, should be preferred to the traditional use of the periodogram, due to the non-stationarity of the data. We argue that this approach can highlight the correlation of spectral measures such as low-frequency and high-frequency HRV with relevant factors as age, gender and Body-Mass-Index, thanks to the improved quality of the spectral measures.
|
|
| 3. |
- Anderson, Rachele, et al.
(författare)
-
Modelling of time-varying HRV using locally stationary processes
- 2017
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Estimates of heart rate variability (HRV), and particularly parameters related to high frequency HRV (HF-HRV), are in-creasingly used as a proxy of cardiac parasympathetic nervous system regulation. Reduced HF-HRV is related to attention deficits, depression, various anxiety disorders, long-term work related stress or burnout, and cardiovascular diseases [1,2]. In this work, a stochastic model, known as Locally Stationary Processes, [3], is applied to HRV data sequences from 47 test participants. The model parameters are estimated with a novel inference method and regression using a number of available covariates is used to investigate their correlation with the stochastic model parameters.
|
|
| 4. |
- Anderson, Rachele, et al.
(författare)
-
Stochastic Modeling and Optimal Time-Frequency Estimation of Task-Related HRV
- 2019
-
Ingår i: Applied Sciences (Switzerland). - : MDPI AG. - 2076-3417 .- 1454-5101. ; 9:23
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper, we propose a novel framework for the analysis of task-related heart rate variability (HRV). Respiration and HRV are measured from 92 test participants while performing a chirp-breathing task consisting of breathing at a slowly increasing frequency under metronome guidance. A non-stationary stochastic model, belonging to the class of Locally Stationary Chirp Processes, is used to model the task-related HRV data, and its parameters are estimated with a novel inference method. The corresponding optimal mean-square error (MSE) time-frequency spectrum is derived and evaluated both with the individually estimated model parameters and the common process parameters. The results from the optimal spectrum are compared to the standard spectrogram with different window lengths and the Wigner-Ville spectrum, showing that the MSE optimal spectral estimator may be preferable to the other spectral estimates because of its optimal bias and variance properties. The estimated model parameters are considered as response variables in a regression analysis involving several physiological factors describing the test participants’ state of health, finding a correlation with gender, age, stress, and fitness. The proposed novel approach consisting of measuring HRV during a chirp-breathing task, a corresponding time-varying stochastic model, inference method, and optimal spectral estimator gives a complete framework for the study of task-related HRV in relation to factors describing both mental and physical health and may highlight otherwise overlooked correlations. This approach may be applied in general for the analysis of non-stationary data and especially in the case of task-related HRV, and it may be useful to search for physiological factors that determine individual differences.
|
|
