Lempel-Ziv complexity of the pNN<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>x</mml:mi></mml:math> statistics – an application to neonatal stress

Šapina, Matej; Karmakar, Chandan; Kramarić, Karolina; Kośmider, Marcin; Garcin, Matthieu; Brdarić, Dario; Milas, Krešimir; Yearwood, John

doi:10.1016/j.chaos.2021.110703

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…LZ complexity measures the nonparametric complexity of the irregularity of time series [ 36 ]. LZ complexity evaluates the algorithmic complexity and determines the emergence rate of new patterns [ 37 ]. A greater LZ complexity means the new changes occur at a faster rate, indicating data change irregularly.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Emotions from Brain Signals on 3D VAD Space via Artificial Intelligence Techniques

2023

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Recent achievements have made emotion studies a rising field contributing to many areas, such as health technologies, brain–computer interfaces, psychology, etc. Emotional states can be evaluated in valence, arousal, and dominance (VAD) domains. Most of the work uses only VA due to the easiness of differentiation; however, very few studies use VAD like this study. Similarly, segment comparisons of emotion analysis with handcrafted features also use VA space. At this point, we primarily focused on VAD space to evaluate emotions and segmentations. The DEAP dataset is used in this study. A comprehensive analytical approach is implemented with two sub-studies: first, segmentation (Segments I–VIII), and second, binary cross-comparisons and evaluations of eight emotional states, in addition to comparisons of selected segments (III, IV, and V), class separation levels (5, 4–6, and 3–7), and unbalanced and balanced data with SMOTE. In both sub-studies, Wavelet Transform is applied to electroencephalography signals to separate the brain waves into their bands (α, β, γ, and θ bands), twenty-four attributes are extracted, and Sequential Minimum Optimization, K-Nearest Neighbors, Fuzzy Unordered Rule Induction Algorithm, Random Forest, Optimized Forest, Bagging, Random Committee, and Random Subspace are used for classification. In our study, we have obtained high accuracy results, which can be seen in the figures in the second part. The best accuracy result in this study for unbalanced data is obtained for Low Arousal–Low Valence–High Dominance and High Arousal–High Valence–Low Dominance emotion comparisons (Segment III and 4.5–5.5 class separation), and an accuracy rate of 98.94% is obtained with the IBk classifier. Data-balanced results mostly seem to outperform unbalanced results.

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Section: Methodsmentioning

confidence: 99%