HRVFrame: Java-Based Framework for Feature Extraction from Cardiac Rhythm,

Abstract. Heart rate variability (HRV) analysis can be successfully applied to automatic classification of cardiac rhythm abnormalities. This paper presents a novel Java-based computer framework for feature extraction from cardiac rhythms. The framework called HRVFrame implements more than 30 HRV linear time domain, frequency domain, time-frequency domain, and nonlinear features. Output of the framework in the form of .arff files enables easier medical knowledge discovery via platforms such as RapidMiner or Weka. The scope of the framework facilitates comparison of models for different cardiac disorders. Some of the features implemented in the framework can also be applied to other biomedical time-series. The thorough approach to feature extraction pursued in this work is also encouraged for other types of biomedical time-series

Evaluating and comparing performance of feature combinations of heart rate variability measures for cardiac rhythm classification

Abstract Automatic classification of cardiac arrhythmias using heart rate variability (HRV) analysis has been an important research topic in recent years. Explorations reveal that various HRV feature combinations can provide highly accurate models for some rhythm disorders. However, the proposed feature combinations lack a direct and carefully designed comparison. The goal of this work is to assess the various HRV feature combinations in classification of cardiac arrhythmias. In this setting, a total of 56 known HRV features are grouped in eight feature combinations. We evaluate and compare the combinations on a difficult problem of automatic classification between nine types of cardiac rhythms using three classification algorithms: support vector machines, AdaBoosted C4.5, and random forest. The effect of analyzed segment length on classification accuracy is also examined. The results demonstrate that there are three combinations that stand out the most, with total classification accuracy of roughly 85% on time segments of 20 seconds duration. A simple combination of time domain features is shown to be comparable to the more informed combinations, with only 1-4% worse results on average than the three best ones. Random forest and AdaBoosted C4.5 are shown to be comparably accurate, while support vector machines was less accurate (4-5%) on this problem. We conclude that the nonlinear features exhibit only a minor influence on the overall accuracy in discerning different arrhythmias. The analysis also shows that reasonably accurate arrhythmia classification lies in the range of 10 to 40 seconds, with a peak at 20 seconds, and a significant drop after 40 seconds

Echocardiographically derived effective valve opening area in mitral prostheses: a comparative analysis of various calculations using continuity equation and pressure half time method

Bogunovic N, Horstkotte D, Faber L, Bogunovic L, van Buuren F. Echocardiographically derived effective valve opening area in mitral prostheses: a comparative analysis of various calculations using continuity equation and pressure half time method. Heart and Vessels. 2016;31(6):932-938.Detection of dysfunctional mitral valve prostheses (MP) remains complex even though being optimized by considering echocardiographically derived prosthetic effective orifice area (VA). The purpose was to compare VA in MP, calculated by the continuity equation (CE) using peak velocities (CEVpeak), mean velocities (CEVmean), velocity–time integrals (CEVTI) and the pressure half time method using 220 ms as constant first (PHT220) as well as optimized constants. In 267 consecutive patients with normally functioning MP, we investigated VA within the first postoperative month. With increasing prosthetic sizes, mean VA values also increase in all calculations. The statistical curves demonstrate no significant difference in graphical steepness but show different levels. Comparison of mean VA showed the known systematic higher values of PHT220 and significantly decreased results when using CEVTI. This systematic difference between mean VA applying PHT220 versus CEVTI is approximately 1.0 cm2 for all prosthetic sizes. Calculations via CEVpeak were close to the results of CEVTI. CEVmean produced values, which graphically correspond to the PHT220 curve. Only PHT220 detected the constructional equal prosthetic inner ring width between 29 and 31 mm. To compensate the systematic difference between CEVTI and PHT220, an optimized constant of 140 ms was calculated to be applied in PHT (PHT140). VA is a robust and, therefore, preferable parameter for investigating MP. If needed, both CE and PHT are applicable with a systematical difference between CEVTI and PHT220. An optimized constant of 140 ms (PHT140) should be applied when calculating VA of mitral valve prostheses via PHT

Physiological left ventricular segmental myocardial mechanics: Multi-parametric polar mapping to determine intraventricular gradients of myocardial dynamics

Bogunovic N, van Buuren F, Esdorn H, Horstkotte D, Bogunovic L, Faber L. Physiological left ventricular segmental myocardial mechanics: Multi-parametric polar mapping to determine intraventricular gradients of myocardial dynamics. ECHOCARDIOGRAPHY-A JOURNAL OF CARDIOVASCULAR ULTRASOUND AND ALLIED TECHNIQUES. 2018;35(12):1947-1955.Objective: We investigated physiological systolic left ventricular (LV) myocardial mechanics and gradients to provide a database for later studies of diseased hearts. Methods: The analyses were performed in 131 heart-healthy individuals and included seven parameters of myocardial mechanics using speckle tracking echocardiography (STE). Results: Basal to apical and circumferentially significant physiological intraventricular parameter gradients of myocardial activity were determined. Global mean values and segmental ranges were peak systolic longitudinal strain -21.2 +/- 3.3%, 95% confidence interval [CI] -21.8% to -20.6%), gradient (basal to apical) -16.0% to -26.7%; peak systolic longitudinal strain rate -1.24 +/- 0.31%/s, 95% CI -1.29% to -1.19%/s, gradient (basal to apical) -0.91% to -1.61%/s; post-systolic index 2.6 +/- 3.2%, 95% CI 3.15%-2.05%, gradient (basal/medial/apical) 7.0/1.2/2.4%; pre-systolic stretch index 1.3 +/- 2.7%, 95% CI 1.77%-0.83%, gradient (basal/medial/apical) 6.5/0.2/1.3%; peak longitudinal displacement 12.2 +/- 2.6 mm, 95% CI 12.6-11.8 mm, gradient (basal to apical) 21.0-3.4 mm; time-to-peak longitudinal strain 370 +/- 43 ms, 95% CI 377-363 ms, gradient (basal to apical) 396-361 ms; and time-to-peak longitudinal strain rate 180 +/- 47 ms, 95% CI 188-172 ms, gradient (basal to apical) 150-200 ms. Conclusion: This study generated a database of seven STE-derived parameters of physiological segmental and global myocardial LV mechanics. The resulting sets of three-dimensional intraventricular mappings of the entire LV provide physiological parameter gradients in baso-apical and circumferential direction by applying the 17-segment polar model. This will facilitate comparison of systolic myocardial activity of the healthy LV with diseased or otherwise altered (eg, sports) hearts