Optimising approximate entropy for assessing cardiac dyssynchrony with radionuclide ventriculography

2021 ◽  
Vol 68 ◽  
pp. 102703
Author(s):  
K.A. Jones ◽  
C.A. Paterson ◽  
D.J. Hamilton ◽  
A.D. Small ◽  
W. Martin ◽  
...  
Keyword(s):  
Approximate Entropy ◽  
Radionuclide Ventriculography ◽  
Cardiac Dyssynchrony

scholarly journals Radionuclide ventriculography phase analysis for risk stratification of patients undergoing cardiotoxic cancer therapy

2020 ◽  
Author(s):  
K. A. Jones ◽  
A. D. Small ◽  
S. Ray ◽  
D. J. Hamilton ◽  
W. Martin ◽  
...  
Keyword(s):  
Cancer Therapy ◽  
Ejection Fraction ◽  
Phase Analysis ◽  
Approximate Entropy ◽  
Radionuclide Ventriculography ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Diagnostic Tools ◽  
Ventricular Ejection Fraction ◽  
Risk Of Cancer

Abstract Background Accurate diagnostic tools to identify patients at risk of cancer therapy-related cardiac dysfunction (CTRCD) are critical. For patients undergoing cardiotoxic cancer therapy, ejection fraction assessment using radionuclide ventriculography (RNVG) is commonly used for serial assessment of left ventricular (LV) function. Methods In this retrospective study, approximate entropy (ApEn), synchrony, entropy, and standard deviation from the phase histogram (phase SD) were investigated as potential early markers of LV dysfunction to predict CTRCD. These phase parameters were calculated from the baseline RNVG phase image for 177 breast cancer patients before commencing cardiotoxic therapy. Results Of the 177 patients, 11 had a decline in left ventricular ejection fraction (LVEF) of over 10% to an LVEF below 50% after treatment had commenced. This patient group had a significantly higher ApEn at baseline to those who maintained a normal LVEF throughout treatment. Of the parameters investigated, ApEn was superior for predicting the risk of CTRCD. Combining ApEn with the baseline LVEF further improved the discrimination between the groups. Conclusions The results suggest that RNVG phase analysis using approximate entropy may aid in the detection of sub-clinical LV contraction abnormalities, not detectable by baseline LVEF measurement, predicting a subsequent decline in LVEF.

Effects of Rest and Exercise on Cardiac Blood Volume Determinations

1997 ◽  
Vol 36 (08) ◽  
pp. 259-264
Author(s):  
N. Topuzović
Keyword(s):  
Radionuclide Ventriculography ◽  
Left Ventricular ◽  
Peak Exercise ◽  
Volume Determination ◽  
Group I ◽  
Lv Volumes ◽  
Group Ii ◽  
Lv Volume

Summary Aim: The purpose of this study was to investigate the changes in blood activity during rest, exercise and recovery, and to assess its influence on left ventricular (LV) volume determination using the count-based method requiring blood sampling. Methods: Forty-four patients underwent rest-stress radionuclide ventriculography; Tc-99m-human serum albumin was used in 13 patients (Group I), red blood cells was labeled using Tc-99m in 17 patients (Group II) in vivo, and in 14 patients (Group III) by modified in vivo/in vitro method. LV volumes were determined by a count-based method using corrected count rate in blood samples obtained during rest, peak exercise and after recovery. Results: In group I at stress, the blood activity decreased by 12.6 ± 5.4%, p <0.05, as compared to the rest level, and increased by 25.1 ± 6.4%, p <0.001, and 12.8 ± 4.5%, p <0.05, above the resting level in group II and III, respectively. This had profound effects on LV volume determinations if only one rest blood aliquot was used: during exercise, the LV volumes significantly decreased by 22.1 ± 9.6%, p <0.05, in group I, whereas in groups II and III it was significantly overestimated by 32.1 ± 10.3%, p <0.001, and 10.7 ± 6.4%, p <0.05, respectively. The changes in blood activity between stress and recovery were not significantly different for any of the groups. Conclusion: The use of only a single blood sample as volume aliquot at rest in rest-stress studies leads to erroneous estimation of cardiac volumes due to significant changes in blood radioactivity during exercise and recovery.

New Hybrid Method for Left Ventricular Ejection Fraction Assessment from Radionuclide Ventriculography Images

2020 ◽  
Vol 16 ◽  
Author(s):  
Halima Dziri ◽  
Mohamed Ali Cherni ◽  
Dorra Ben Sellem
Keyword(s):  
Left Ventricular Ejection Fraction ◽  
Left Ventricle ◽  
Ejection Fraction ◽  
Positive Predictive Value ◽  
Roc Curve ◽  
Radionuclide Ventriculography ◽  
Left Ventricular ◽  
The Other ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction

Background: In this paper, we propose a new efficient method of radionuclide ventriculography image segmentation to estimate the left ventricular ejection fraction. This parameter is an important prognostic factor for diagnosing abnormal cardiac function. Methods: The proposed method combines the Chan-Vese and the mathematical morphology algorithms. It was applied to diastolic and systolic images obtained from the Nuclear Medicine Department of Salah AZAIEZ Institute.In order to validate our proposed method, we compare the obtained results to those of two methods of the literature. The first one is based on mathematical morphology, while the second one uses the basic Chan-Vese algorithm. To evaluate the quality of segmentation, we compute accuracy, positive predictive value and area under the ROC curve. We also compare the left ventricle ejection fraction estimated by our method to that of the reference given by the software of the gamma-camera and validated by the expert, using Pearson’s correlation coefficient, ANOVA test and linear regression. Results and conclusion: Static results show that the proposed method is very efficient in the detection of the left ventricle. The accuracy was 98.60%, higher than that of the other two methods (95.52% and 98.50%). Likewise, the positive predictive value was the highest (86.40% vs. 83.63% 71.82%). The area under the ROC curve was also the most important (0.998% vs. 0.926% 0.919%). On the other hand, Pearson's correlation coefficient was the highest (99% vs. 98% 37%). The correlation was significantly positive (p<0.001).

scholarly journals HRV analysis: undependability of approximate entropy at locating optimum complexity in malnourished children

2021 ◽  
pp. 1-6
Author(s):  
David M. Garner ◽  
Gláucia S. Barreto ◽  
Vitor E. Valenti ◽  
Franciele M. Vanderlei ◽  
Andrey A. Porto ◽  
...  
Keyword(s):  
Statistical Significance ◽  
Accurate Determination ◽  
Approximate Entropy ◽  
Effect Sizes ◽  
Potential Hazard ◽  
Autonomic Modulation ◽  
Embedding Dimension ◽  
Malnourished Children ◽  
Hrv Analysis

Abstract Introduction: Approximate Entropy is an extensively enforced metric to evaluate chaotic responses and irregularities of RR intervals sourced from an eletrocardiogram. However, to estimate their responses, it has one major problem – the accurate determination of tolerances and embedding dimensions. So, we aimed to overt this potential hazard by calculating numerous alternatives to detect their optimality in malnourished children. Materials and methods: We evaluated 70 subjects split equally: malnourished children and controls. To estimate autonomic modulation, the heart rate was measured lacking any physical, sensory or pharmacologic stimuli. In the time series attained, Approximate Entropy was computed for tolerance (0.1→0.5 in intervals of 0.1) and embedding dimension (1→5 in intervals of 1) and the statistical significances between the groups by their Cohen’s ds and Hedges’s gs were totalled. Results: The uppermost value of statistical significance accomplished for the effect sizes for any of the combinations was −0.2897 (Cohen’s ds) and −0.2865 (Hedges’s gs). This was achieved with embedding dimension = 5 and tolerance = 0.3. Conclusions: Approximate Entropy was able to identify a reduction in chaotic response via malnourished children. The best values of embedding dimension and tolerance of the Approximate Entropy to identify malnourished children were, respectively, embedding dimension = 5 and embedding tolerance = 0.3. Nevertheless, Approximate Entropy is still an unreliable mathematical marker to regulate this.

scholarly journals Quantification of early learning and movement sub-structure predictive of motor performance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Vikram Jakkamsetti ◽  
William Scudder ◽  
Gauri Kathote ◽  
Qian Ma ◽  
Gustavo Angulo ◽  
...  
Keyword(s):  
Motor Performance ◽  
Support Vector Machine Classifier ◽  
Motor Impairment ◽  
Approximate Entropy ◽  
Video Tracking ◽  
Support Vector ◽  
Learning Support ◽  
Arc Length ◽  
Vertical Movements ◽  
Movement Smoothness

AbstractTime-to-fall off an accelerating rotating rod (rotarod) is widely utilized to evaluate rodent motor performance. We reasoned that this simple outcome could be refined with additional measures explicit in the task (however inconspicuously) to examine what we call movement sub-structure. Our goal was to characterize normal variation or motor impairment more robustly than by using time-to-fall. We also hypothesized that measures (or features) early in the sub-structure could anticipate the learning expected of a mouse undergoing serial trials. Using normal untreated and baclofen-treated movement-impaired mice, we defined these features and automated their analysis using paw video-tracking in three consecutive trials, including paw location, speed, acceleration, variance and approximate entropy. Spectral arc length yielded speed and acceleration uniformity. We found that, in normal mice, paw movement smoothness inversely correlated with rotarod time-to-fall for the three trials. Greater approximate entropy in vertical movements, and opposite changes in horizontal movements, correlated with greater first-trial time-to-fall. First-trial horizontal approximate entropy in the first few seconds predicted subsequent time-to-fall. This allowed for the separation, after only one rotarod trial, of different-weight, untreated mouse groups, and for the detection of mice otherwise unimpaired after baclofen, which displayed a time-to-fall similar to control. A machine-learning support vector machine classifier corroborated these findings. In conclusion, time-to-fall off a rotarod correlated well with several measures, including some obtained during the first few seconds of a trial, and some responsive to learning over the first two trials, allowing for predictions or preemptive experimental manipulations before learning completion.

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