Range entropy: A bridge between signal complexity and self-similarity

Approximate entropy (ApEn) and sample entropy (SampEn) are widely used for temporal complexity analysis of real-world phenomena. However, their relationship with the Hurst exponent as a measure of self-similarity is not widely studied. Additionally, ApEn and SampEn are susceptible to signal amplitude changes. A common practice for addressing this issue is to correct their input signal amplitude by its standard deviation. In this study, we first show, using simulations, that ApEn and SampEn are related to the Hurst exponent in their tolerance r and embedding dimension m parameters. We then propose a modification to ApEn and SampEn called range entropy or RangeEn. We show that RangeEn is more robust to nonstationary signal changes, and it has a more linear relationship with the Hurst exponent, compared to ApEn and SampEn. RangeEn is bounded in the tolerance r-plane between 0 (maximum entropy) and 1 (minimum entropy) and it has no need for signal amplitude correction. Finally, we demonstrate the clinical usefulness of signal entropy measures for characterisation of epileptic EEG data as a real-world example.Comment: This is the revised and published version in Entrop

Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer's disease

Objective. This study aims to explore the potential of high-resolution brain functional connectivity based on electroencephalogram, a non-invasive low-cost technique, to be translated into a long-overdue biomarker and a diagnostic method for Alzheimer's disease (AD). Approach. The paper proposes a novel ultra-high-resolution time-frequency nonlinear cross-spectrum method to construct a promising biomarker of AD pathophysiology. Specifically, using the peak frequency estimated from a revised Hilbert–Huang transformation (RHHT) cross-spectrum as a biomarker, the support vector machine classifier is used to distinguish AD from healthy controls (HCs). Main results. With the combinations of the proposed biomarker and machine learning, we achieved a promising accuracy of 89%. The proposed method performs better than the wavelet cross-spectrum and other functional connectivity measures in the temporal or frequency domain, particularly in the Full, Delta and Alpha bands. Besides, a novel visualisation approach developed from topography is introduced to represent the brain functional connectivity, with which the difference between AD and HCs can be clearly displayed. The interconnections between posterior and other brain regions are obviously affected in AD. Significance. Those findings imply that the proposed RHHT approach could better track dynamic and nonlinear functional connectivity information, paving the way for the development of a novel diagnostic approach

Longitudinal ALS study: research of EEG biomarkers during the progression of the disease

ALS is a neurodegenerative disorder that brings patients to a state of complete paralysis. In this thesis a longitudinal analysis of EEG resting state data is performed for three patients, setting a signal processing pipeline to detect which features of the signal are changing over the observation period. It has been found a substantial difference in the spectral content of EEG signal between late-stage patients and the one observed during the transition to completely locked-in state (CLIS)

Tracking brain dynamics across transitions of consciousness

How do we lose and regain consciousness? The space between healthy wakefulness and unconsciousness encompasses a series of gradual and rapid changes in brain activity. In this thesis, I investigate computational measures applicable to the electroencephalogram to quantify the loss and recovery of consciousness from the perspective of modern theoretical frameworks. I examine three different transitions of consciousness caused by natural, pharmacological and pathological factors: sleep, sedation and coma. First, I investigate the neural dynamics of falling asleep. By combining the established methods of phase-lag brain connectivity and EEG microstates in a group of healthy subjects, a unique microstate is identified, whose increased duration predicts behavioural unresponsiveness to auditory stimuli during drowsiness. This microstate also uniquely captures an increase in frontoparietal theta connectivity, a putative marker of the loss of consciousness prior to sleep onset. I next examine the loss of behavioural responsiveness in healthy subjects undergoing mild and moderate sedation. The Lempel-Ziv compression algorithm is employed to compute signal complexity and symbolic mutual information to assess information integration. An intriguing dissociation between responsiveness and drug level in blood during sedation is revealed: responsiveness is best predicted by the temporal complexity of the signal at single- channel and low-frequency integration, whereas drug level is best predicted by the complexity of spatial patterns and high-frequency integration. Finally, I investigate brain connectivity in the overnight EEG recordings of a group of patients in acute coma. Graph theory is applied on alpha, theta and delta networks to find that increased variability in delta network integration early after injury predicts the eventual coma recovery score. A case study is also described where the re-emergence of frontoparietal connectivity predicted a full recovery long before behavioural improvement. The findings of this thesis inform prospective clinical applications for tracking states of consciousness and advance our understanding of the slow and fast brain dynamics underlying its transitions. Collating these findings under a common theoretical framework, I argue that the diversity of dynamical states, in particular in temporal domain, and information integration across brain networks are fundamental in sustaining consciousness.My PhD was funded by the Cambridge Trust and a MariaMarina award from Lucy Cavendish College

Nonlinear dynamics and modeling of heart and brain signals

Ph.DDOCTOR OF PHILOSOPH

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Complexity Science in Human Change

This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

Connectivité fonctionnelle des générateurs de deux types d'ondes lentes dans une population jeune et âgée

Le cerveau endormi tend à se déconnecter dans sa progression vers le sommeil lent (SL) chez les jeunes adultes et se déconnecte moins chez les plus âgés. Les ondes lentes (OL) sont les caractéristiques principales du sommeil lent sur l’électroencéphalogramme (EEG). Notre groupe a récemment montré que deux types d’OL co-existent, les « slow switcher » (SlowS) et les « fast switcher » (FastS), caractérisées par leur vitesse de transition entre les maximums d’hyperpolarisation et de dépolarisation. Sur l’EEG, la connectivité globale pendant la transition des SlowS et des FastS diffère et diminue avec le vieillissement. Dans cette étude, nous utilisons des enregistrements de magnétoencéphalographie pour évaluer les changements relatifs à l’âge sur les générateurs des OL pendant la transition entre les maximums d’hyperpolarisation et de dépolarisation en termes de 1) topographie et 2) connectivité, avec l’indice de délais de phase pondéré basé sur le délai de phase moyen dans la transition des OL. Nous avons fait l’hypothèse que comparativement aux OL des individus jeunes, les OL des individus plus âgés vont 1) impliquer des régions corticales plus étendues et 2) montrer plus de connectivité, spécialement pour les SlowS. Nos résultats révèlent que comparativement aux jeunes participants, les plus vieux montrent 1) plus d’implication du précuneus droit pendant les SlowS et 2) une connectivité globale supérieure, surtout pour les SlowS. Finalement, les individus plus jeunes montrent plus de connectivité que les individus plus âgés entre des régions spécifiques, plus précisément dans le réseau antéropostérieur pour les SlowS que les FastS. Ensemble, nos résultats suggèrent une perte de flexibilité des réseaux pendant la transition des OL chez les individus plus âgés par rapport aux individus plus jeunes.The sleeping brain tends to disconnect as it progresses toward slow wave sleep (SWS) in young adults and disconnects less in older adults. Slow waves (SW) are the main characteristics of slow wave sleep on the electroencephalogram (EEG). Our group recently showed that two types of SW co-exist, the “slow switcher” (SlowS) and the “fast switcher” (FastS), characterized by the transition speed between the hyperpolarized and depolarized peaks. On the EEG, the global connectivity during the transition of the SlowS and FastS differs and is reduced with aging. In this study, we used magnetoencephalography recordings to investigate age-related differences on the SW generators during the transition between the hyperpolarized and depolarized peaks in terms of 1) topography and 2) connectivity, using the weighted phase lag index based on the average phase lag during the SW transition. We hypothesised that as compared to younger individuals, SW of older participants would 1) involve broader cortical areas and 2) show higher connectivity than younger individuals, particularly for the SlowS. Our results revealed that as compared to younger participants, older individuals showed 1) more involvement of the right precuneus during the SlowS and 2) globally higher connectivity, more significantly for the SlowS. Finally, younger individuals showed higher connectivity than older individuals between specific regions, more precisely in the anteroposterior network for the SlowS than the FastS. Altogether, our results suggest an impaired flexibility of the network during the SW transition in older individuals as compared to younger individuals

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis