The nonlinear dynamic methods could have clinical and prognostic applicability also in short-time ECG series. Dynamic analysis based on chaos theory during the exercise ECG test point out the multifractal time series in CHD patients who loss normal fractal characteristics and regularity in HRV. Nonlinear analysis technique may complement traditional ECG analysis.

Complexity-based analyses may quantify abnormalities in heart rate variability (HRV). The aim of this study was to investigate the clinical and prognostic significances of dynamic HRV changes in patients with stress-induced cardiomyopathy Takotsubo syndrome (TS) by means of linear and nonlinear analysis. Patients with TS were included in study after complete noninvasive and invasive cardiovascular diagnostic evaluation and compared to an age and gender matched control group of healthy subjects. Series of R-R interval and of ST-T interval values were obtained from 24-h ECG recordings after digital sampling. HRV analysis was performed by 'range rescaled analysis' to determine the Hurst exponent, by detrended fluctuation analysis to quantify fractal long-range correlation properties, and by approximate entropy to assess time-series predictability. Short- and long-term fractal-scaling exponents were significantly higher in patients with TS in acute phases, opposite to lower approximate entropy and Hurst exponent, but all variables normalized in a few weeks. Dynamic HRV analysis allows assessing changes in complexity features of HRV in TS patients during the acute stage, and to monitor recovery after treatment, thus complementing traditional ECG and clinically analysis.

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This paper presents a case study of the process of insightful analysis of clinical data collected in regular hospital practice. The approach is applied to a database describing patients suffering from brain ischaemia, either permanent as brain stroke with positive computer tomography (CT) or reversible ischaemia with normal brain CT test. The goal of the analysis is the extraction of useful knowledge that can help in diagnosis, prevention and better understanding of the vascular brain disease. This paper demonstrates the applicability of subgroup discovery for insightful data analysis and describes the expert's process of converting the induced rules into useful medical knowledge. Detection of coexistThis work was supported by Croatian Ministry of Science, Education and Sport project "Machine Learning Algorithms and Applications", Slovenian Ministry of Higher Education, Science and Technology project "Knowledge Technologies", and EU FP6 project "Heartfaid: A knowledge based platform of services for supporting medical-clinical management of the heart failure within the elderly population".ing risk factors, selection of relevant discriminative points for numerical descriptors, as well as the detection and description of characteristic patient subpopulations are important results of the analysis. Graphical representation is extensively used to illustrate the detected dependencies in the available clinical data.

Abstract. Contrast set mining aims at finding differences between different groups. This paper shows that a contrast set mining task can be transformed to a subgroup discovery task whose goal is to find descriptions of groups of individuals with unusual distributional characteristics with respect to the given property of interest. The proposed approach to contrast set mining through subgroup discovery was successfully applied to the analysis of records of patients with brain stroke (confirmed by a positive CT test), in contrast with patients with other neurological symptoms and disorders (having normal CT test results). Detection of coexisting risk factors, as well as description of characteristic patient subpopulations are important outcomes of the analysis.