Biomedical subjects

Mika P Tarvainen

Publications and source records attributed to Mika P Tarvainen.

7 recordsLinked to original sources

PubMed2006

Time-varying analysis of heart rate variability signals with a Kalman smoother algorithm.

A time-varying parametric spectrum estimation method for analysing non-stationary heart rate variability signals is presented. As a case study, the dynamics of heart rate variability during an orthostatic test is examined. In this method, the non-stationary signal is first modelled with a time-varying autoregressive model and the model parameters are estimated recursively with a Kalman smoother algorithm. The benefit of using the Kalman smoother is that the lag error present in a Kalman filter, as well as in all other adaptive filters, can be avoided. The spectrum estimates for each time instant are then obtained from the estimated model parameters. Statistics of the obtained spectrum estimates are derived using the error propagation principle. The obtained spectrum estimates can further be decomposed into separate components and, thus, the time variation of low- and high-frequency components of heart rate variability can be examined separately. By using the presented method, high resolution time-varying spectrum estimates with no lag error can be produced. Other benefits of the method are the straightforward procedure for evaluating the statistics of the spectrum estimates and the option of spectral decomposition.

Adult↗

PubMed2005

High-resolution QRS fiducial point corrections in sparsely sampled ECG recordings.

A model based high-resolution QRS fiducial point correction algorithm, which is suitable for sparsely sampled electrocardiogram (ECG) recordings, is presented. The presented method can be divided into three steps. First, the initial QRS fiducial points are estimated by using ordinary interpolation methods. Then, the data points of each QRS are extracted and centered in time and the shape of the QRS complex is estimated by nonlinearly fitting a double exponential function to the extracted data points. Finally, the estimated model and its derivative are linearly fitted to the data points of each QRS complex separately and new fiducial point estimates are obtained. The proposed method is tested with simulations and real ECG data. As a result, it is observed that the proposed method is also suitable for asymmetric QRS complexes unlike, e.g., the commonly used cubic spline interpolation method.

Computer Simulation↗

PubMed2005

Single-trial dynamical estimation of event-related potentials: a Kalman filter-based approach.

A method for single-trial dynamical estimation of event-related potentials (ERPs) is presented. The method is based on recursive Bayesian mean square estimation and the estimators are obtained with a Kalman filtering procedure. We especially focus on the case that previous trials contain prior information of relevance to the trial being analyzed. The potentials are estimated sequentially using the previous estimates as prior information. The performance of the method is evaluated with simulations and with real P300 responses measured using auditory stimuli. Our approach is shown to have excellent capability of estimating dynamic changes form stimulus to stimulus present in the parameters of the ERPs, even in poor signal-to-noise ratio (SNR) conditions.

Algorithms↗

PubMed2004

Software for advanced HRV analysis.

A computer program for advanced heart rate variability (HRV) analysis is presented. The program calculates all the commonly used time- and frequency-domain measures of HRV as well as the nonlinear Poincaré plot. In frequency-domain analysis parametric and nonparametric spectrum estimates are calculated. The program generates an informative printable report sheet which can be exported to various file formats including the portable document format (PDF). Results can also be saved as an ASCII file from which they can be imported to a spreadsheet program such as the Microsoft Excel. Together with a modern heart rate monitor capable of recording RR intervals this freely distributed program forms a complete low-cost HRV measuring and analysis system.

Algorithms↗

PubMed2004

Estimation of nonstationary EEG with Kalman smoother approach: an application to event-related synchronization (ERS).

An adaptive spectrum estimation method for nonstationary electroencephalogram by means of time-varying autoregressive moving average modeling is presented. The time-varying parameter estimation problem is solved by Kalman filtering along with a fixed-interval smoothing procedure. Kalman filter is an optimal filter in the mean square sense and it is a generalization of other adaptive filters such as recursive least squares or least mean square. Furthermore, by using the smoother the unavoidable tracking lag of adaptive filters can be avoided. Due to the properties of Kalman filter and benefits of the smoothing the time-frequency resolution of the presented Kalman smoother spectra is extremely high. The presented approach is applied to estimation of event-related synchronization/desynchronization (ERS/ERD) dynamics of occipital alpha rhythm measured from three healthy subjects. With the Kalman smoother approach detailed spectral information can be extracted from single ERS/ERD samples.

Algorithms↗

PubMed2003

Heart rate variability in acute psychosis.

Heart rate variability (HRV) provides reliable tools to assess the integrity and reactivity of autonomic nervous function. Our aim was to examine HRV in the resting condition and during different mental loads in acute psychosis compared to healthy controls. HRV was measured in 17 first-episode drug-naive patients with psychosis and 21 healthy controls during oddball tasks and while performing the Wisconsin Card Sorting Test. A discrete event series was constructed by an adaptive QRS detector algorithm and power spectrum estimation was carried out. The RMSSD (representing interval differences of successive heartbeats) and the amount of high frequency (HF) power were significantly reduced in patients. Moreover, the patients' HRV remained unaltered during the tasks, whereas in controls the HRV diminished with increasing mental load of the task. Patients with psychosis displayed less short-term HR reactivity than healthy controls. They also failed to adapt HRV according to the task-connected strain. Acute psychosis is characterized by a limited capacity to respond to external demands at the level of autonomic nervous system.

Acute Disease↗

PubMed2002

An advanced detrending method with application to HRV analysis.

An advanced, simple to use, detrending method to be used before heart rate variability analysis (HRV) is presented. The method is based on smoothness priors approach and operates like a time-varying finite-impulse response high-pass filter. The effect of the detrending on time- and frequency-domain analysis of HRV is studied.

Algorithms↗