Publications

The severe neurological disorder epilepsy affects almost 1% of the world population. For patients who suffer from pharmacoresistant focal-onset epilepsy, electroencephalographic (EEG) recordings are essential for the localization of the brain area where seizures start. Apart from the visual inspection of the recordings, quantitative EEG signal analysis techniques proved to be useful for this purpose. Among other features, regularity versus irregularity and phase coherence versus phase independence allowed characterizing brain dynamics from the measured EEG signals. Can phase irregularities also characterize brain dynamics? To address this question, we use the univariate coefficient of phase velocity variation, defined as the ratio of phase velocity standard deviation and the mean phase velocity. Beyond that, as a bivariate measure we use the classical mean phase coherence to quantify the degree of phase locking. All phase-based measures are combined with surrogates to test null hypotheses about the dynamics underlying the signals. In the first part of our analysis, we use the Rössler model system to study our approach under controlled conditions. In the second part, we use the Bern-Barcelona EEG database which consists of focal and nonfocal signals extracted from seizure-free recordings. Focal signals are recorded from brain areas where the first seizure EEG signal changes can be detected, and nonfocal signals are recorded from areas that are not involved in the seizure at its onset. Our results show that focal signals have less phase variability and more phase coherence than nonfocal signals. Once combined with surrogates, the mean phase velocity proved to have the highest discriminative power between focal and nonfocal signals. In conclusion, conceptually simple and easy to compute phase-based measures can help to detect features induced by epilepsy from EEG signals. This holds not only for the classical mean phase coherence but even more so for univariate measures of phase irregularity. © 2022 American Physical Society.

JTD Keywords: brain, entropy, epileptogenic networks, functional connectivity, hilbert transform, seizure onset, surrogate data, synchronization, time-series, Biomedical signal processing, Brain areas, Brain dynamics, Dynamics, Electroencephalographic signals, Electroencephalography, Electrophysiology, Intracranial eeg signals, Localisation, Neurological disorders, Neurology, Phase based, Phase coherence, Signal detection, Simple++, Univariate, Velocity, World population

Of all the various locomotion strategies in low- (Formula presented.), traveling-wave propulsion methods with an elastic tail are preferred because they can be developed using simple designs and fabrication procedures. The only intrinsic property of the elastic tail that governs the form and rate of wave propagation along its length is the bending stiffness. Such traveling wave motion is performed by spermatozoa, which possess a tail that is characterized by intrinsic variable stiffness along its length. In this paper, the passive bending stiffness of the magnetic nanoparticle-coated flagella of bull sperm cells is measured using a contactless electromagnetic-based excitation method. Numerical elasto-hydrodynamic models are first developed to predict the magnetic excitation and relaxation of nanoparticle-coated nonuniform flagella. Then solutions are provided for various groups of nonuniform flagella with disparate nanoparticle coatings that relate their bending stiffness to their decay rate after the magnetic field is removed and the flagellum restores its original configuration. The numerical models are verified experimentally, and capture the effect of the nanoparticle coating on the bending stiffness. It is also shown that electrostatic self-assembly enables arbitrarily magnetizable cellular segments with variable stiffness along the flagellum. The bending stiffness is found to depend on the number and location of the magnetized cellular segments. © 2022 The Authors. Advanced Theory and Simulations published by Wiley-VCH GmbH.

JTD Keywords: cilia, flagella, flagellar propulsion, low reynolds numbers, magnetic, microswimmers, passive, sperm cell, Bending stiffness, Cells, Cellulars, Coatings, Decay (organic), Electric excitation, Excited states, Flagellar propulsion, Locomotion strategies, Low reynolds numbers, Magnetic, Magnetic excitations, Nanoparticle coatings, Passive, Propulsion methods, Self assembly, Simple++, Sperm cell, Sperm cells, Stiffness, Travelling waves, Variable stiffness, Wave propagation, Younǵs modulus

Patients with spinal cord injury (SCI) have an increased risk of sleep-disordered breathing (SDB), which can lead to serious comorbidities and impact patients’ recovery and quality of life. However, sleep tests are rarely performed on SCI patients, given their multiple health needs and the cost and complexity of diagnostic equipment. The objective of this study was to use a novel smartphone system as a simple non-invasive tool to monitor SDB in SCI patients. We recorded pulse oximetry, acoustic, and accelerometer data using a smartphone during overnight tests in 19 SCI patients and 19 able-bodied controls. Then, we analyzed these signals with automatic algorithms to detect desaturation, apnea, and hypopnea events and monitor sleep position. The apnea–hypopnea index (AHI) was significantly higher in SCI patients than controls (25 ± 15 vs. 9 ± 7, p < 0.001). We found that 63% of SCI patients had moderate-to-severe SDB (AHI ? 15) in contrast to 21% of control subjects. Most SCI patients slept predominantly in supine position, but an increased occurrence of events in supine position was only observed for eight patients. This study highlights the problem of SDB in SCI and provides simple cost-effective sleep monitoring tools to facilitate the detection, understanding, and management of SDB in SCI patients.

JTD Keywords: apnea syndrome, biomedical signal processing, individuals, mhealth, monitoring, nasal resistance, people, position, prevalence, questionnaire, sample, sleep apnea, sleep position, sleep-disordered breathing, smartphone, time, Apnea-hypopnea indices, Biomedical signal processing, Biomedical signals processing, Cost effectiveness, Diagnosis, Mhealth, Monitoring, Noninvasive medical procedures, Oximeters, Oxygen-saturation, Patient rehabilitation, Simple++, Sleep apnea, Sleep position, Sleep research, Sleep-disordered breathing, Smart phones, Smartphone, Smartphones, Spinal cord injury, Spinal cord injury patients