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Advanced methods of heart rate signals processing and their usefulness in diagnosis support II. Univariate statistical techniques

Moczko J. A.

Computational Methods in Science and Technology

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2004

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Vol. 10, nr 1

73-82

EN

A tentative cardiological database was established using virtual instrumentation described in the first part of presented paper. Some additional not heart rate variability parameters were added. Three selected univariate statistical techniques were used for illustration diagnosis support techniques in discrimination between healthy and coronary heart disease people. Comparison of nonparametric Mann-Whitney test, receiver operating characteristic ROC analysis and univariate logistic regression results was performed. In all used methods long term heart rate variability indices were most useful in prediction of patient's status. The correctness of classification was between 55 to 79 percent with ROC technique and 68 to 78 percent with logistic regression. However high number of false negative FN cases excludes univariate techniques as reliable screening test.

2

Application bootstrapping Kaplan - Meier estimate for survival curve smoothing

Michalak M., Powidzki M., Moczko J. A.

Computational Methods in Science and Technology

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2002

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Vol. 8, nr 2

58--64

EN

In this article we wish to present and encourage the other to use bootstrap methods in statistical analysis. We show how to bootstrap Kaplan-Meier estimator and pay attention to its advantage opposite to classical analysis. Then we present simulation study and survival time of second remission of patients suffering for acute leukaemia.

3

Advanced methods of heart rate signals processing and their usefulness in diagnosis support. I, Mathematical heart rate descriptors and virtual instrumentation

Moczko J. A.

Computational Methods in Science and Technology

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2002

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Vol. 8, nr 2

65--76

EN

The working heart is the source of electrical signal which carries the basic information about homeostasis process of human being. After appropriate acquisition and preprocessing electrocardiogram (ECG) signal may be analyzed in different ways revealing not only current status of atrial and ventricular electrical activity of heart muscle but also information about sympathetic and parasympathetic nervous system, thermoregulation and vasomotor tone, renin-angiotensin control systems, baroreceptor reflex and blood pressure regulation, respiratory activity etc. Heart rate variability (HRV) defined as a timing of the sequence of QRS complexes is one of the most promising quantitative markers of autonomic activity. Different mathematical indices in time and frequency domain were designed to extract full details valuable for diagnostic purposes. Presented paper is the first one of the series of articles describing mathematical methodology of heart rate (HR) signal processing and further classification of obtained markers. The ultimate goal is to find optimal set of HR descriptors which support diagnosis in different cardiac pathologies.