This study proposes a quantitative measurement of split of the second heart sound (S2) based on nonstationary signal decomposition to deal with overlaps and energy modeling of the subcomponents of S2. The second heart sound includes aortic (A2) and pulmonic (P2) closure sounds. However, the split detection is obscured due to A2-P2 overlap and low energy of P2. To identify such split, HVD method is used to decompose the S2 into a number of components while preserving the phase information. Further, A2s and P2s are localized using smoothed pseudo Wigner-Ville distribution followed by reassignment method. Finally, the split is calculated by taking the differences between the means of time indices of A2s and P2s. Experiments on total 33 clips of S2 signals are performed for evaluation of the method. The mean ± standard deviation of the split is 34.7 ± 4.6 ms. The method measures the split efficiently, even when A2-P2 overlap is ≤ 20 ms and the normalized peak temporal ratio of P2 to A2 is low (≥ 0.22). This proposed method thus, demonstrates its robustness by defining split detectability (SDT), the split detection aptness through detecting P2s, by measuring up to 96 percent. Such findings reveal the effectiveness of the method as competent against the other baselines, especially for A2-P2 overlaps and low energy P2.
This work presents a comprehensive review on stimuli presentation, which is an important stage of any emotion elicitation experiment in affect analysis. Due to lack of standard guidelines, the researchers employ their self-devised methods which are not always sufficiently informative-making this area very inconsistent and ambiguous. In addition, an ample study about this stage including how to select, design and present the stimuli has not been reported properly earlier. In this purpose, an inclusive study has been conducted aiming to summarize various aspects of stimuli presentation including type of stimuli, available database, presentation tools, subjective measures, ethical issues and so on. Certainly, among several methods of emotion recognition (e.g., facial expression, speech, gesture and physiological signal), the EEG based emotion recognition works have been considered here due to availability of sufficient number of works, reliability and well-established technology. In total, 137 peer reviewed articles have been studied and the results show that about 83% of emotion elicitations have been performed by employing visual stimuli (mostly pictures and video). Therefore, presentation of visual stimuli has been explored with great emphasis covering laboratory setup, presentation timing, subjective issues, and ethical issues. Finally, an extensive recommendations regarding stimuli presentation has been provided which could guide to conduct the emotion elicitation experiments effectively.
We present a new large-scale three-fold annotated microscopy image dataset, aiming to advance the plant cell biology research by exploring different cell microstructures including cell size and shape, cell wall thickness, intercellular space, etc. in deep learning (DL) framework. This dataset includes 9,811 unstained and 6,127 stained (safranin-o, toluidine blue-o, and lugol’s-iodine) images with three-fold annotation including physical, morphological, and tissue grading based on weight, different section area, and tissue zone respectively. In addition, we prepared ground truth segmentation labels for three different tuber weights. We have validated the pertinence of annotations by performing multi-label cell classification, employing convolutional neural network (CNN), VGG16, for unstained and stained images. The accuracy has been achieved up to 0.94, while, F2-score reaches to 0.92. Furthermore, the ground truth labels have been verified by semantic segmentation algorithm using UNet architecture which presents the mean intersection of union up to 0.70. Hence, the overall results show that the data are very much efficient and could enrich the domain of microscopy plant cell analysis for DL-framework.
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