BIOSIGNAL and MEDICAL IMAGE PROCESSING

Semmlow, John L.; Griffel, Benjamin

doi:10.1201/b16584

Cited by 14 publications

(20 citation statements)

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“…This technique relies on the assumption that the signal is influenced by both short-term and long-term features. For a proper interpretation of the effects hidden behind the internal dynamics, the signal is supposed to be analyzed at multiple time scales . A brief description of the original DFA algorithm is presented below.…”

Section: Methodsmentioning

confidence: 99%

“…For a proper interpretation of the effects hidden behind the internal dynamics, the signal is supposed to be analyzed at multiple time scales. 41 A brief description of the original DFA algorithm is presented below. The procedure starts with the calculation of the profile y i as the cumulative sum of the data x i with the subtracted mean ⟨x ⟩…”

Section: ■ Methodsmentioning

confidence: 99%

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Multifractal Properties of BK Channel Currents in Human Glioblastoma Cells

Wawrzkiewicz-Jałowiecka

Trybek

Dworakowska

et al. 2020

J. Phys. Chem. B

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Potassium channels play an important physiological role in glioma cells. In particular, voltage- and Ca 2+ -activated large-conductance BK channels (gBK in gliomas) are involved in the intensive growth and extensive migrating behavior of the mentioned tumor cells; thus, they may be considered as a drug target for the therapeutic treatment of glioblastoma. To enable appropriate drug design, molecular mechanisms of gBK channel activation by diverse stimuli should be unraveled as well as the way that the specific conformational states of the channel relate to its functional properties (conducting/nonconducting). There is an open debate about the actual mechanism of BK channel gating, including the question of how the channel proteins undergo a range of conformational transitions when they flicker between nonconducting (functionally closed) and conducting (open) states. The details of channel conformational diffusion ought to have its representation in the properties of the experimental signal that describes the ion-channel activity. Nonlinear methods of analysis of experimental nonstationary series can be useful for observing the changes in the number of channel substates available from geometrical and energetic points of view at given external conditions. In this work, we analyze whether the multifractal properties of the activity of glioblastoma BK channels depend on membrane potential, and which states, conducting or nonconducting, affect the total signal to a larger extent. With this aim, we carried out patch-clamp experiments at different levels of membrane hyper- and depolarization. The obtained time series of single channel currents were analyzed using the multifractal detrended fluctuation analysis (MFDFA) method in a standard form and incorporating focus-based multifractal (FMF) formalism. Thus, we show the applicability of a modified MFDFA technique in the analysis of an experimental patch-clamp time series. The obtained results suggest that membrane potential strongly affects the conformational space of the gBK channel proteins and the considered process has nonlinear multifractal characteristics. These properties are the inherent features of the analyzed signals due to the fact that the main tendencies vanish after shuffling the data.

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Section: Methodsmentioning

confidence: 99%

Section: ■ Methodsmentioning

confidence: 99%

Multifractal Properties of BK Channel Currents in Human Glioblastoma Cells

Wawrzkiewicz-Jałowiecka

Trybek

Dworakowska

et al. 2020

J. Phys. Chem. B

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“…PCA is a method that transforms a ser of correlated variables into a new set of uncorrelated variables in principal components. It involves determining the covariance matrix of the data set and subsequently decomposing the matrix using eigenvalue decomposition into the eigenvector matric and eigenvalues [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

“…The k-Nearest Neighbours (k-NN) classifier is one of the simplest and most straightforward machine learning algorithms to implement. This classifier is distance-based and produces a model directly based on the available training data [ 38 ]. The classifier calculates the distance, in the feature space, between a given test point and the points in the training set, producing the k-nearest points.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Models for Weight-Bearing Activity Type Recognition Based on Accelerometry in Postmenopausal Women

Huggins

Clarke

Abásolo

et al. 2022

Sensors

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Hip-worn triaxial accelerometers are widely used to assess physical activity in terms of energy expenditure. Methods for classification in terms of different types of activity of relevance to the skeleton in populations at risk of osteoporosis are not currently available. This publication aims to assess the accuracy of four machine learning models on binary (standing and walking) and tertiary (standing, walking, and jogging) classification tasks in postmenopausal women. Eighty women performed a shuttle test on an indoor track, of which thirty performed the same test on an indoor treadmill. The raw accelerometer data were pre-processed, converted into eighteen different features and then combined into nine unique feature sets. The four machine learning models were evaluated using three different validation methods. Using the leave-one-out validation method, the highest average accuracy for the binary classification model, 99.61%, was produced by a k-NN Manhattan classifier using a basic statistical feature set. For the tertiary classification model, the highest average accuracy, 94.04%, was produced by a k-NN Manhattan classifier using a feature set that included all 18 features. The methods and classifiers within this study can be applied to accelerometer data to more accurately characterize weight-bearing activity which are important to skeletal health.

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“…Typically, medical data includes the information collected from patients, such as personal data, diagnostic information, and collected biosignals and bioimages [ 1 – 3 ]. Assessment of medical data collected from the patient is essential during diagnosis, treatment execution, and monitoring of the recovery rate.…”

Section: Introductionmentioning

confidence: 99%

VGG-UNet/VGG-SegNet Supported Automatic Segmentation of Endoplasmic Reticulum Network in Fluorescence Microscopy Images

et al. 2022

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This research work aims to implement an automated segmentation process to extract the endoplasmic reticulum (ER) network in fluorescence microscopy images (FMI) using pretrained convolutional neural network (CNN). The threshold level of the raw FMT is complex, and extraction of the ER network is a challenging task. Hence, an image conversion procedure is initially employed to reduce its complexity. This work employed the pretrained CNN schemes, such as VGG-UNet and VGG-SegNet, to mine the ER network from the chosen FMI test images. The proposed ER segmentation pipeline consists of the following phases; (i) clinical image collection, 16-bit to 8-bit conversion and resizing; (ii) implementation of pretrained VGG-UNet and VGG-SegNet; (iii) extraction of the binary form of ER network; (iv) comparing the mined ER with ground-truth; and (v) computation of image measures and validation. The considered FMI dataset consists of 223 test images, and image augmentation is then implemented to increase these images. The result of this scheme is then confirmed against other CNN methods, such as U-Net, SegNet, and Res-UNet. The experimental outcome confirms a segmentation accuracy of >98% with VGG-UNet and VGG-SegNet. The results of this research authenticate that the proposed pipeline can be considered to examine the clinical-grade FMI.

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BIOSIGNAL and MEDICAL IMAGE PROCESSING

Cited by 14 publications

References 0 publications

Multifractal Properties of BK Channel Currents in Human Glioblastoma Cells

Multifractal Properties of BK Channel Currents in Human Glioblastoma Cells

Machine Learning Models for Weight-Bearing Activity Type Recognition Based on Accelerometry in Postmenopausal Women

VGG-UNet/VGG-SegNet Supported Automatic Segmentation of Endoplasmic Reticulum Network in Fluorescence Microscopy Images

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