Pasi A. Karjalainen

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.

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.

Kubios HRV - Heart rate variability analysis software

Kubios HRV is an advanced and easy to use software for heart rate variability (HRV) analysis. The software supports several input data formats for electrocardiogram (ECG) data and beat-to-beat RR interval data. It includes an adaptive QRS detection algorithm and tools for artifact correction, trend removal and analysis sample selection. The software computes all the commonly used time-domain and frequency-domain HRV parameters and several nonlinear parameters. There are several adjustable analysis settings through which the analysis methods can be optimized for different data. The ECG derived respiratory frequency is also computed, which is important for reliable interpretation of the analysis results. The analysis results can be saved as an ASCII text file (easy to import into MS Excel or SPSS), Matlab MAT-file, or as a PDF report. The software is easy to use through its compact graphical user interface. The software is available free of charge for Windows and Linux operating systems at http://kubios.uef.fi.

Tikhonov regularization and prior information in electrical impedance tomography

The solution of impedance distribution in electrical impedance tomography is a nonlinear inverse problem that requires the use of a regularization method. The generalized Tikhonov regularization methods have been popular in the solution of many inverse problems. The regularization matrices that are usually used with the Tikhonov method are more or less ad hoc and the implicit prior assumptions are, thus, in many cases inappropriate. In this paper, the authors propose an approach to the construction of the regularization matrix that conforms to the prior assumptions on the impedance distribution. The approach is based on the construction of an approximating subspace for the expected impedance distributions. It is shown by simulations that the reconstructions obtained with the proposed method are better than with two other schemes of the same type when the prior is compatible with the true object. On the other hand, when the prior is incompatible with the true object, the method will still give reasonable estimates.

Increased fMRI responses during encoding in mild cognitive impairment

Structural and functional magnetic resonance imaging (fMRI) was performed on 21 healthy elderly controls, 14 subjects with mild cognitive impairment (MCI) and 15 patients with mild Alzheimers disease (AD) to investigate changes in fMRI activation in relation to underlying structural atrophy. The fMRI paradigm consisted of associative encoding of novel picture-word pairs. Structural analysis of the brain was performed using voxel-based morphometry (VBM) and hippocampal volumetry. Compared to controls, the MCI subjects exhibited increased fMRI responses in the posterior hippocampal, parahippocampal and fusiform regions, while VBM revealed more atrophy in MCI in the anterior parts of the left hippocampus. Furthermore, the hippocampal volume and parahippocampal activation were negatively correlated in MCI, but not in controls or in AD. We suggest that the increased fMRI activation in MCI in the posterior medial temporal and closely connected fusiform regions is compensatory due to the incipient atrophy in the anterior medial temporal lobe.

A Kalman filter approach to track fast impedance changes in electrical impedance tomography

In electrical impedance tomography (EIT), an estimate for the cross-sectional impedance distribution is obtained from the body by using current and voltage measurements made from the boundary. All well-known reconstruction algorithms use a full set of independent current patterns for each reconstruction. In some applications, the impedance changes may be so fast that information on the time evolution of the impedance distribution is either lost or severely blurred. Here, the authors propose an algorithm for EIT reconstruction that is able to track fast changes in the impedance distribution. The method is based on the formulation of EIT as a state-estimation problem and the recursive estimation of the state with the aid of the Kalman filter. The performance of the proposed method is evaluated with a simulation of human thorax in a situation in which the impedances of the ventricles change rapidly. The authors show that with optimal current patterns and proper parameterization, the proposed approach yields significant enhancement of the temporal resolution over the conventional reconstruction strategy.

Kubios HRV — A Software for Advanced Heart Rate Variability Analysis

A software for advanced heart rate variability (HRV) analysis is presented. The software includes adaptable tools for correcting artifacts and for removing low frequency trend components. The analysis options of the software include all the commonly used time-domain, frequency-domain and nonlinear HRV parameters. Analysis results can be saved as a PDF report, ASCII text file or Matlab MAT file. The software is easy to use through its compact graphical user interface. Together with a high-quality heart rate monitor, capable of recording beat-to-beat RR intervals, this freely distributed software forms a complete system for HRV analysis.

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.

Assessment of errors in static electrical impedance tomography with adjacent and trigonometric current patterns

In electrical impedance tomography (EIT), difference imaging is often preferred over static imaging. This is because of the many unknowns in the forward modelling which make it difficult to obtain reliable absolute resistivity estimates. However, static imaging and absolute resistivity values are needed in some potential applications of EIT. In this paper we demonstrate by simulation the effects of different error components that are included in the reconstruction of static EIT images. All simulations are carried out in two dimensions with the so-called complete electrode model. Errors that are considered are the modelling error in the boundary shape of an object, errors in the electrode sizes and localizations and errors in the contact impedances under the electrodes. Results using both adjacent and trigonometric current patterns are given.

Electrical impedance tomography with basis constraints

In this paper, we consider the impedance tomography problem of estimating the conductivity distribution within the body from static current/voltage measurements on the bodys surface. We present a new method of implementing prior information of the conductivities in the optimization algorithm. The method is based on the approximation of the prior covariance matrix by simulated samples of feasible conductivities. The reduction of the dimensionality of the optimization problem is performed by principal component analysis (PCA).