| 1. |
- Anderson, Rachele, et al.
(författare)
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Classification of EEG signals based on mean-square error optimal time-frequency features
- 2018
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Ingår i: 2018 26th European Signal Processing Conference, EUSIPCO 2018. - 9789082797015 ; 2018-September, s. 106-110
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Konferensbidrag (refereegranskat)abstract
- This paper illustrates the improvement in accuracy of classification for electroencephalogram (EEG) signals measured during a memory encoding task, by using features based on a mean square error (MSE) optimal time-frequency estimator. The EEG signals are modelled as Locally Stationary Processes, based on the modulation in time of an ordinary stationary covariance function. After estimating the model parameters, we compute the MSE optimal kernel for the estimation of the Wigner-Ville spectrum. We present a simulation study to evaluate the performance of the derived optimal spectral estimator, compared to the single windowed Hanning spectrogram and the Welch spectrogram. Further, the estimation procedure is applied to the measured EEG and the time-frequency features extracted from the spectral estimates are used to feed a neural network classifier. Consistent improvement in classification accuracy is obtained by using the features from the proposed estimator, compared to the use of existing methods.
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| 2. |
- 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
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Ingår i: Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies. - : SCITEPRESS. - 9789897582790 ; , s. 17-25
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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 consid- ering 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 parameterswith age, BMI, State and Trait Anxiety as well as stress level.
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| 3. |
- Anderson, Rachele, et al.
(författare)
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Effects of Age, BMI, anxiety and stress on the parameters of a stochastic model for heart rate variability including respiratory information
- 2018
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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
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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.
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| 4. |
- Anderson, Rachele, et al.
(författare)
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Inference for time-varying signals using locally stationary processes
- 2019
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Ingår i: Journal of Computational and Applied Mathematics. - : Elsevier BV. - 0377-0427. ; 347, s. 24-35
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Tidskriftsartikel (refereegranskat)abstract
- Locally Stationary Processes (LSPs) in Silverman’s sense, defined by the modulation in time of a stationary covariance function, are valuable in stochastic modelling of time-varying signals. However, for practical applications, methods to conduct reliable parameter inference from measured data are required. In this paper, we address the lack of suitable methods for estimating the parameters of the LSP model, by proposing a novel inference method. The proposed method is based on the separation of the two factors defining the LSP covariance function, in order to take advantage of their individual structure and divide the inference problem into two simpler sub-problems. The method’s performance is tested in a simulation study and compared with traditional sample covariance based estimation. An illustrative example of parameter estimation from EEG data, measured during a memory encoding task, is provided.
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| 5. |
- Anderson, Rachele, et al.
(författare)
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Insights on Spectral Measures for HRV Based on a Novel Approach for Data Acquisition
- 2018
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Ingår i: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. - 1557-170X. ; 2018, s. 510-513
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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.
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| 6. |
- Anderson, Rachele
(författare)
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Modelling and Inference using Locally Stationary Processes : Biomedical applications
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis considers statistical methods for non-stationary signals, specifically stochastic modelling, inference on the model parameters and optimal spectral estimation. The models are based on Silverman’s definition of Locally Stationary Processes (LSPs). In all the contributions, an example of a biomedical application of the proposed method is provided. In the first two papers, the methods are applied to electroencephalography (EEG) data, while in the third paper the application involves Heart Rate Variability (HRV) data.In paper A, we propose a method for estimating the parameters of an LSP model. The proposed method is based on the separation of the two factors defining the LSP covariance function, in order to take advantage of their individual structure and divide the inference problem into two simpler sub-problems. We present a simulation study to show the method’s performance in terms of speed of convergence, accuracy and robustness. Finally, we provide an illustrative example of parameter estimation from three sets of EEG signals, measured from one person during several trials of a memory encoding task of three different categories of visual memories.Paper B investigates the estimation of the Wigner-Ville spectrum of time-varying processes, modelled as LSPs. Previous works have provided the theoretical expression of the mean-square error optimal time-frequency kernel, and now, thanks to the introduced inference method, we are able to compute the optimal kernel in real data cases. The derived optimal spectral estimator is compared with the single Hanning spectrogram and the Welch method in a simulation study. As biomedical application, we compute the optimal spectral estimate according to the estimated model parameters for the three EEG data-sets alsotreated in paper A.In paper C, we model HRV signals with a model known as Locally Stationary Chirp Processes (LSCP), which is an extension of the LSP model including the presence of a chirp. The inference method proposed in paper A is adapted to take into account the respiratory signal information, in the form of the covariance matrix of the chirp respiratory signal estimated beforehand. We perform least squares regression analysis with each of the LSCP model parameters as the response to explore the correlation of the parameters with several factors of interest. Our results show a statistically significant correlation between the model parameters with age, BMI, State and Trait Anxiety as well as stress level.
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| 7. |
- Anderson, Rachele, et al.
(författare)
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Modelling of time-varying HRV using locally stationary processes
- 2017
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Ingår i: Abstract book. ; , s. 44-
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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 asLocally 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.
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| 8. |
- Anderson, Rachele, et al.
(författare)
-
Modelling of time-varying HRV using locally stationary processes
- 2017
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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.
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| 9. |
- Anderson, Rachele, et al.
(författare)
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Multitaper Spectral Granger Causality with Application to Ssvep
- 2020
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Ingår i: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing. - 9781509066315 ; , s. 1284-1288
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Konferensbidrag (refereegranskat)abstract
- The traditional parametric approach to Granger causality (GC), based on linear vector autoregressive modeling, suffers from difficulties related to the inaccurate modeling of the generative process. These limits can be solved by using nonparametric spectral estimates in the frequency-domain formulation of GC, also known as spectral GC. In a simulation study, we compare the mean square error of the estimated spectral GC using different multitaper spectral estimators, finding that the Peak Matched multitapers are preferable for estimating spectral GC characterized by peaks. As an illustrative example, we apply the non-parametric approach to the analysis of brain functional connectivity in steady-state visually evoked potentials.
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| 10. |
- Anderson, Rachele
(författare)
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Statistical inference and time-frequency estimation for non-stationary signal classification
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis focuses on statistical methods for non-stationary signals. The methods considered or developed address problems of stochastic modeling, inference, spectral analysis, time-frequency analysis, and deep learning for classification. In all the contributions, an example of a biomedical application of the proposed method is provided, either to electroencephalography (EEG) data or to Heart Rate Variability (HRV) data. Four manuscripts are included in this Ph.D. thesis.
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