SummaryBackgroundThis paper presents a software package for quantitative evaluation of heart rate variability (HRV), heart rate turbulence (HRT), and T-wave alternans (TWA) from ECG recordings. The software has been developed for the purpose of scientific research rather than clinical diagnosis.Material/MethodsThe software is written in Matlab Mathematical Language. Procedures for evaluation of HRV, HRT and TWA were implemented. HRV analysis was carried out by applying statistical and spectral parametric and nonparametric methods. HRT parameters were derived using the Schmidt algorithm. TWA analysis was performed both in spectral and in time domain by applying Poincare mapping. A flexibility of choosing from a number of classical modelling approaches and their modifications was foreseen and implemented. The software underwent preliminary verification tests both on ECGs from the Physionet online ECG signal repository and recordings taken at the Department of Electrocardiology of the Medical University Hospital in Lodz.ResultsThe result of the research is a program enabling simultaneous analysis of a number of parameters computed from ECG recordings with the use of the indicated analysis methods. The program offers options to preview the intermediate results and to alter the preprocessing steps.ConclusionsBy offering the possibility to cross-validate the results of analyses obtained by several methods and to preview the intermediate analysis steps, the program can serve as a helpful aid for clinicians in comprehensive research studies. The software tool can also be utilized in training programs for students and medical personnel.
An algorithm for parametric modelling of a specific type of time series, namely a series of time intervals is proposed and discussed in the paper. The necessary preprocessing steps are presented. They include timebase computation, interpolation, re-sampling and, depending on the application, detrending. The proposed approach of parametric modelling is based on autoregressive moving average (ARMA) model. The methods for ARMA model derivation assume that the model orders (the lengths of autoregressive and moving average filters) are known a priori. Since the result is highly sensitive to the order choice, it is necessary to establish rules for proper order selection. The solution to this, often underestimated, problem is also addressed in the article.
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