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Mann-Whitney and Student T-test for patients with hypertrophic cardiomyopathy, coronary artery disease and aortic valve stenosis with cells marked in green for which we rejected the null hypothesis.
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Objective.The physiological activity of the heart is controlled and modulated mostly by the parasympathetic and sympathetic nervous systems. Heart rate variability (HRV) analysis is therefore used to observe fluctuations that reflect changes in the activity in these two branches. Knowing that acceleration and deceleration patterns in heart rate flu...
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... recordings in which the data was not normally distributed, we used a Mann-Whitney test because of the different number of cases in the groups. Our results for Mann-Whitney and Student T-test were shown in Table 6. From the statistical tests we see that there are significant statistical differences between the following patient groups: ...Similar publications
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... The best solutions obtained by APSOIIM and other common meta-heuristics are presented in table 13. Table 13 compares APSOIIM with SCA [8] and other techniques (modified SCA (m-SCA) [49] and CONLIN [51]). The data indicate that the proposed algorithm can find the most suitable decision variables to derive the optimal fitness value, that is, the best solution suited to the practical engineering application. ...
This paper proposes an adaptive particle swarm optimization with information interaction mechanism (APSOIIM) to enhance the optimization ability of the PSO algorithm. Firstly, a chaotic sequence strategy is employed to generate uniformly distributed particles and to improve their convergence speed at the initialization stage of the algorithm. Then, an interaction information mechanism is introduced to boost the diversity of the population as the search process unfolds, which can effectively interact with the optimal information of neighboring particles to enhance the exploration and exploitation abilities. Therefore, the proposed algorithm may avoid premature and perform a more accurate local search. Besides, the convergence was proven to verify the robustness and efficiency of the proposed APSOIIM algorithm. Finally, the proposed APSOIIM was applied to solve the CEC2014 and CEC2017 benchmark functions as well as famous engineering optimization problems. The experimental results demonstrate that the proposed APSOIIM has significant advantages over the compared algorithms.
... Finally, only commonly-used complexity features have been explored in this work. Recently, newer methods have emerged, such as bubble entropy (Manis et al., 2017) and multi-fractal measures (Kokosińska et al., 2021); these could lead to additional insights and should be explored in the future. ...
Prediction of mental states, such as stress and anxiety, can be important in situations where reduced job performance due to increased mental strain can lead to critical situations (e.g., front-line healthcare workers and first responders). While recent advances in biomedical wearable sensor technologies have allowed for collection of multiple physiological signals in everyday environments, numerous challenges emerge from such uncontrolled settings, including increased noise levels and artifacts, confounding effects from other psychological states (e.g., mental fatigue), as well as physical variables (e.g., physical activity). These factors can be particularly detrimental for heart rate variability (HRV) measures which, in controlled settings, have been shown to accurately track stress and anxiety states. In this paper, we propose two new ways of computing HRV proxies which we show are more robust to such artifacts and confounding factors. The proposed features measure spectral and complexity properties of different aspects of the autonomic nervous system, as well as their interaction. Across two separate “in-the-wild” datasets, the proposed features showed to not only outperform benchmark HRV metrics, but to also provide complementary information, thus leading to significantly greater accuracy levels when fused together. Feature ranking analysis further showed the proposed features appearing in 45–64% of the top features, thus further emphasizing their importance. In particular, features derived from the high frequency band showed to be most important in the presence of fatigue and physical activity confounding factors, thus corroborating their importance for mental state assessment in highly ecological settings.
