Выбор оптимального количества бинов для расчета взаимной информации между сигналами ЭЭГ и кардиоритмограммы

В роботі розглянуто метод визначення взаємної інформації для оцінки взаємозв’язку між сигналами ЕЕГ та кардіоритмограми. У розробленому методі кількість бінів вибирається базуючись на значеннях взаємної інформації, які розраховані на проміжку значень бінів. При застосуванні методу до сигналів ЕЕГ та КРГ було знайдено, що метод може бути застосований при аналізі взаємозв’язку між сигналами ЕЕГ в каналах, що розташовані поряд або симетрично, згідно з системою 10-20. Для взаємної інформації між сигналами ЕЕГ та КРГ, що пов’язані у значно меншій мірі, метод не може бути застосований для малого обсягу вибірки.In the present work the problem of optimal bin number selection for equidistant Mutual Information (MI) estimator between electroencephalogram (EEG) and cardiorhythmogram (CRG) is addressed. In the previously developed method the bin number selected based on the finding an optimal bin number on the MI values on the range of bin numbers. With application to the real raw EEG and CRG signals it was found that for closely placed or symmetrical channels of EEG data the method can be applied, and the true value of MI value can be found with proposed method. In application to MI calculation between raw EEG and CRG signals that are not significantly coupled, true MI value cannot be estimated with proposed method for small sample size.В работе рассмотрен метод определения взаимной информации для оценки взаимосвязи между сигналами ЭЭГ и кардиоритмограммы. В разработанном методе количество бинов выбирается на промежутке значений бинов. При применении метода к сигналам ЭЭГ и КРГ было найдено, что метод может быть применим при анализе взаимосвязи между сигналами ЭЭГ в каналах, что расположены рядом или симметрично, в соответствии с системой 10-20. Для взаимной информации между сигналами ЭЭГ и КРГ, что связаны в значительно меньшей мере, метод не может применяться для малого значения выборки

Взаимная информация между активностью мозга и сердца перед эпилептическим приступом

Робота присвячена аналізу зв’язку між електричною активністю мозку та серцево-судинної системи людини у хворих на епілепсію в період перед початком епілептичного нападу. Запропоновано кількісно оцінювати зв’язок з використанням взаємної інформації між повними потужностями електроенцефалограми та кардіоритмограми в часових вікнах протягом години до початку нападу. Результати клінічних досліджень для десяти сигналів, які містять напади, показали суттєве зниження взаємної інформації за 5 хвилин перед початком нападу.The paper analyzes the relation between the brain and cardiovascular system activity in patients with epilepsy in the period before an epileptic seizure. Quantification of the connection by mutual information between full power of electroencephalogram and cardiorhythmogram in time windows for an hour before the seizure is proposed. Clinical study results for ten signals containing seizures showed a significant reduction of the mutual information in 5 minutes before the seizure.Работа посвящена анализу связи между электрической активностью мозга и сердечно-сосудистой системы у больных эпилепсией в период перед началом эпилептического приступа. Предложено количественно оценивать связь с использованием взаимной информации между полными мощностями электроэцефалограммы и кардиоритмограмы во временных окнах за час до начала приступа. Результаты клинических исследований для десяти сигналов, содержащих приступы, показали существенное снижение взаимной информации за 5 минут перед началом приступа

Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals

[EN] One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR), and by the Generalitat Valenciana (AICO/2019/220)Nieto Del-Amor, F.; Beskhani, R.; Ye Lin, Y.; Garcia-Casado, J.; Díaz-Martínez, MDA.; Monfort-Ortiz, R.; Diago-Almela, VJ.... (2021). Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals. Sensors. 21(18):1-17. https://doi.org/10.3390/s21186071S117211

Adaptive sequential feature selection in visual perception and pattern recognition

In the human visual system, one of the most prominent functions of the extensive feedback from the higher brain areas within and outside of the visual cortex is attentional modulation. The feedback helps the brain to concentrate its resources on visual features that are relevant for recognition, i. e. it iteratively selects certain aspects of the visual scene for refined processing by the lower areas until the inference process in the higher areas converges to a single hypothesis about this scene. In order to minimize a number of required selection-refinement iterations, one has to find a short sequence of maximally informative portions of the visual input. Since the feedback is not static, the selection process is adapted to a scene that should be recognized. To find a scene-specific subset of informative features, the adaptive selection process on every iteration utilizes results of previous processing in order to reduce the remaining uncertainty about the visual scene. This phenomenon inspired us to develop a computational algorithm solving a visual classification task that would incorporate such principle, adaptive feature selection. It is especially interesting because usually feature selection methods are not adaptive as they define a unique set of informative features for a task and use them for classifying all objects. However, an adaptive algorithm selects features that are the most informative for the particular input. Thus, the selection process should be driven by statistics of the environment concerning the current task and the object to be classified. Applied to a classification task, our adaptive feature selection algorithm favors features that maximally reduce the current class uncertainty, which is iteratively updated with values of the previously selected features that are observed on the testing sample. In information-theoretical terms, the selection criterion is the mutual information of a class variable and a feature-candidate conditioned on the already selected features, which take values observed on the current testing sample. Then, the main question investigated in this thesis is whether the proposed adaptive way of selecting features is advantageous over the conventional feature selection and in which situations. Further, we studied whether the proposed adaptive information-theoretical selection scheme, which is a computationally complex algorithm, is utilized by humans while they perform a visual classification task. For this, we constructed a psychophysical experiment where people had to select image parts that as they think are relevant for classification of these images. We present the analysis of behavioral data where we investigate whether human strategies of task-dependent selective attention can be explained by a simple ranker based on the mutual information, a more complex feature selection algorithm based on the conventional static mutual information and the proposed here adaptive feature selector that mimics a mechanism of the iterative hypothesis refinement. Hereby, the main contribution of this work is the adaptive feature selection criterion based on the conditional mutual information. Also it is shown that such adaptive selection strategy is indeed used by people while performing visual classification.:1. Introduction 2. Conventional feature selection 3. Adaptive feature selection 4. Experimental investigations of ACMIFS 5. Information-theoretical strategies of selective attention 6. Discussion Appendix Bibliograph

A Comparison of Multivariate Mutual Information Estimators for Feature Selection

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