Wojciech Oleksy

Continuous representation of unevenly sampled signals - An application to the analysis of heart rate variability

Various methods for the continuous representation of unevenly sampled physiological signals, especially of heart rate and heart rate variability have been published over the past two decades. Due to the mathematical difficulties of analyzing unevenly spaced event series or unevenly sampled signals, all of the previously described techniques are impeded by some severe limitations which result from either representing the signal based on an assumed and often insufficient physiological model of signal generation, or from simplifications in the selected signal processing tools. Though the described methods have their merits and allow sufficient description of heart rate in some special applications, there has been no systematic investigation into a continuous representation of unevenly sampled physiological signals which is free from any limitations or simplifying assumptions and which would allow an objective comparison of the known methods and the determination of the respective errors in order to decide which technique is the best suited for a specific application. In this paper a continuous representation of the point event series describing the temporal sequence of heart beats is presented which does not impose any limiting assumptions on the data analysis. The results obtained were validated through simulations in both time and frequency domains, and compared with the established techniques to allow the determination of errors at the specific times where the heart beats occur, as well as within the intervals between these sample points.

Improving EASI ECG Method Using Various Machine Learning and Regression Techniques to Obtain New EASI ECG Model

—Main idea of this study was to increase efficiency of the EASI ECG method introduced by Dover in 1988 using various regression techniques. EASI was proven to have high correlation with standard 12 lead ECG. Apart from that it is less susceptible to artefacts, increase mobility of patients and is easier to use because of smaller number of electrodes. were used to improve the quality of the 12-lead electrocardiogram derived from four (EASI) electrodes.

New Diagnostic Tool for Patients Suffering from Noncommunicable Diseases (NCDs)

Electrocardiography, technique, which is an essential tool in the diagnosis of heart disease, as well as other organs, is used by doctors for over 100 years. It is used to measure electrical activity of the heart as a function of time and present it in digital or analogue form. Whilst the standard 12 lead ECG is the basic clinical method of heart diagnosis it has its drawbacks. Measuring all 12 leads is often difficult and impractical, most of all it restricts patient movement. In 1988, Gordon Dower developed a system of quasi-orthogonal lead called EASI, which uses only 5 electrodes in order to register standard 12 lead ECG signals. The main goal of this work is to present a new tool using machine learning algorithms which transforms electrocardiographic signals (ECG) performed by EASI into a standard 12-channel ECG, which therefore could be an ideal tool for diagnose of NCDs.

The Higher-Order Spectra (HOSA) as a Tool for the Rehabilitation Progress Estimation Referred to the Patients Diagnosed with Various Cardiac Diseases

This article explores the possibility of using the higher-order spectra to identify different types of diseases. In order to assess the effectiveness of such tool the HRV (Heart Rate Variability) recordings obtained from patients suffering from three different cardiac problems are listed and compared to the results recorded for healthy subjects. Each set of HRV signals is processed with bispectral and bicoherent analysis. In both cases three statistical parameters are observed. For each type of the investigated analysis the parameters under examination differ enough to allow clear distinction of the specific cardiac disease. The obtained results show usefulness of higher-order spectra as a tool for differentiation between specific diseases. Authors believe that further work would greatly improve potential of the described tool, allowing to identify number of different diseases or even stage of the illness or progress in the rehabilitation process.

Device Based on EASI ECG Method as a Simple and Efficient Tool in Diagnostics of Patients Suffering from Noncommunicable Diseases (NCDs)

Noncommunicable diseases (NCDs) kill 38 million people each year, 17.5 million among them die of cardiovascular diseases [1]. Although for over 100 years we know electrocardiography, technique which is essential tool in diagnosis of heart diseases, as well as other organs, still cardiovascular diseases are the deadliest from all NCDs. In XXI century we have all necessary tools, knowledge and technology to save more lives. Simple electronic device measuring and analysing ECG signal connected to smartphone could be a solution for this dramatic problem.

The higher-order spectra as a tool for the identification of patients diagnosed with various cardiac diseases

This article explores the possibility of using the higher-order spectra to identify different types of diseases. In order to assess the effectiveness of such tool the HRV (Heart Rate Variability) recordings obtained from patients suffering from three different cardiac problems are listed and compared to the results recorded for healthy subjects. Each set of HRV signals is processed with bispectral and bicoherent analysis. In both cases three statistical parameters are observed. For each type of the investigated analysis the parameters under examination differ enough to allow clear distinction of the specific cardiac disease. The obtained results show usefulness of higher-order spectra as a tool for differentiation between specific diseases. Authors believe that further work would greatly improve potential of the described tool, allowing to identify number of different diseases or even stage of the illness or progress in the rehabilitation process.

Improved EASI ECG model obtained using various machine learning and regression techniques

Main idea of this study was to increase efficiency of the EASI ECG method introduced by Dover in 1988 using various regression techniques. EASI was proven to have high correlation with standard 12 lead ECG. Apart from that it is less susceptible to artefacts, increase mobility of patients and is easier to use because of smaller number of electrodes. Multilayer Perceptron (Artificial Neural Network), Support Vector Machines, Linear Regression, Pace Regression and Least Median of Squares Regression methods were used to improve the quality of the 12-lead electrocardiogram derived from four (EASI) electrodes.

Improving EASI ECG Method Using Various Regression Techniques

Main idea of this study was to increase efficiency of the EASI ECG method introduced by Dover in 1988 using various regression techniques. EASI was proven to have high correlation with standard 12 lead ECG. Multilayer Perceptron (Artificial Neural Network), Sequential Minimal Optimization Regression and Linear Regression methods were used to improve the quality of the 12-lead electrocardiogram derived from four (EASI) electrodes. Computation of Root Mean Squared Error and Correlation Coefficient was performed to measure the overall result of a given method. The lowest RMSE of 20,90 and the highest correlation coefficient of 0,9858 were obtained using ANN method. Second best result was obtained for SMO Regression Method with Radial Basis Function kernel (RMSE equal to 25,97 and correlation coefficient of 0,9813). The least complex regression method of Linear Regression produced results on a level of 28,35 for RMSE and 0,9741 for correlation coefficient. Results obtained for classic Dover algorithm of deriving 12-lead ECG from EASI electrodes were much worse than those obtained for all regression methods. RMSE of 80,10, which is by around 59,19 higher than RMSE of ANN and correlation coefficient of 0,96, which is by 0,03 lower then correlation coefficient of ANN.