Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries

Estimating the depth of anaesthesia (DoA) in operations has always been a challenging issue due to the underlying complexity of the brain mechanisms. Electroencephalogram (EEG) signals are undoubtedly the most widely used signals for measuring DoA. In this paper, a novel EEG-based index is proposed to evaluate DoA for 24 patients receiving general anaesthesia with different levels of unconsciousness. Sample Entropy (SampEn) algorithm was utilised in order to acquire the chaotic features of the signals. After calculating the SampEn from the EEG signals, Random Forest was utilised for developing learning regression models with Bispectral index (BIS) as the target. Correlation coefficient, mean absolute error, and area under the curve (AUC) were used to verify the perioperative performance of the p roposed method. Validation comparisons with typical nonstationary signal analysis methods (i.e., recurrence analysis and permutation entropy) and regression methods (i.e., neural network and support vector machine) were conducted. To further verify the accuracy and validity of the proposed methodology, the data is divided into four unconsciousness-level groups on the basis of BIS levels. Subsequently, analysis of variance (ANOVA) was applied to the corresponding index (i.e., regression output). Results indicate that the correlation coefficient improved to 0.72 ± 0.09 after filtering and to 0.90 ± 0.05 after regression from the initial values of 0.51 ± 0.17. Similarly, the final mean absolute error dramatically declined to 5.22 ± 2.12. In addition, the ultimate AUC increased to 0.98 ± 0.02, and the ANOVA analysis indicates that each of the four groups of different anaesthetic levels demonstrated significant difference from the nearest levels. Furthermore, the Random Forest output was extensively linear in relation to BIS, thus with better DoA prediction accuracy. In conclusion, the proposed method provides a concrete basis for monitoring patients' anaesthetic level during surgeries.variou

Incessant transitions between active and silent states in cortico-thalamic circuits and altered neuronal excitability lead to epilepsy

La ligne directrice de nos expériences a été l'hypothèse que l'apparition et/ou la persistance des fluctuations de longue durée entre les états silencieux et actifs dans les réseaux néocorticaux et une excitabilité neuronale modifiée sont les facteurs principaux de l'épileptogenèse, menant aux crises d’épilepsie avec expression comportementale. Nous avons testé cette hypothèse dans deux modèles expérimentaux différents. La déafférentation corticale chronique a essayé de répliquer la déafférentation physiologique du neocortex observée pendant le sommeil à ondes lentes. Dans ces conditions, caractérisées par une diminution de la pression synaptique et par une incidence augmentée de périodes silencieuses dans le système cortico-thalamique, le processus de plasticité homéostatique augmente l’excitabilité neuronale. Par conséquent, le cortex a oscillé entre des périodes actives et silencieuses et, également, a développé des activités hyper-synchrones, s'étendant de l’hyperexcitabilité cellulaire à l'épileptogenèse focale et à des crises épileptiques généralisées. Le modèle de stimulation sous-liminale chronique (« kindling ») du cortex cérébral a été employé afin d'imposer au réseau cortical une charge synaptique supérieure à celle existante pendant les états actifs naturels - état de veille ou sommeil paradoxal (REM). Dans ces conditions un mécanisme différent de plasticité qui s’est exprimé dans le système thalamo-corticale a imposé pour des longues périodes de temps des oscillations continuelles entre les époques actives et silencieuses, que nous avons appelées des activités paroxysmiques persistantes. Indépendamment du mécanisme sous-jacent de l'épileptogenèse les crises d’épilepsie ont montré certaines caractéristiques similaires : une altération dans l’excitabilité neuronale mise en évidence par une incidence accrue des décharges neuronales de type bouffée, une tendance constante vers la généralisation, une propagation de plus en plus rapide, une synchronie augmentée au cours du temps, et une modulation par les états de vigilance (facilitation pendant le sommeil à ondes lentes et barrage pendant le sommeil REM). Les états silencieux, hyper-polarisés, de neurones corticaux favorisent l'apparition des bouffées de potentiels d’action en réponse aux événements synaptiques, et l'influence post-synaptique d'une bouffée de potentiels d’action est beaucoup plus importante par rapport à l’impacte d’un seul potentiel d’action. Nous avons également apporté des évidences que les neurones néocorticaux de type FRB sont capables à répondre avec des bouffées de potentiels d’action pendant les phases hyper-polarisées de l'oscillation lente, propriété qui peut jouer un rôle très important dans l’analyse de l’information dans le cerveau normal et dans l'épileptogenèse. Finalement, nous avons rapporté un troisième mécanisme de plasticité dans les réseaux corticaux après les crises d’épilepsie - une diminution d’amplitude des potentiels post-synaptiques excitatrices évoquées par la stimulation corticale après les crises - qui peut être un des facteurs responsables des déficits comportementaux observés chez les patients épileptiques. Nous concluons que la transition incessante entre des états actifs et silencieux dans les circuits cortico-thalamiques induits par disfacilitation (sommeil à ondes lentes), déafférentation corticale (épisodes ictales à 4-Hz) ou par une stimulation sous-liminale chronique (activités paroxysmiques persistantes) crée des circonstances favorables pour le développement de l'épileptogenèse. En plus, l'augmentation de l’incidence des bouffées de potentiels d’actions induisant une excitation post-synaptique anormalement forte, change l'équilibre entre l'excitation et l'inhibition vers une supra-excitation menant a l’apparition des crises d’épilepsie.The guiding line in our experiments was the hypothesis that the occurrence and / or the persistence of long-lasting fluctuations between silent and active states in the neocortical networks, together with a modified neuronal excitability are the key factors of epileptogenesis, leading to behavioral seizures. We addressed this hypothesis in two different experimental models. The chronic cortical deafferentation replicated the physiological deafferentation of the neocortex observed during slow-wave sleep (SWS). Under these conditions of decreased synaptic input and increased incidence of silent periods in the corticothalamic system the process of homeostatic plasticity up-regulated cortical cellular and network mechanisms and leaded to an increased excitability. Therefore, the deafferented cortex was able to oscillate between active and silent epochs for long periods of time and, furthermore, to develop highly synchronized activities, ranging from cellular hyperexcitability to focal epileptogenesis and generalized seizures. The kindling model was used in order to impose to the cortical network a synaptic drive superior to the one naturally occurring during the active states - wake or rapid eye movements (REM) sleep. Under these conditions a different plasticity mechanism occurring in the thalamo-cortical system imposed long-lasting oscillatory pattern between active and silent epochs, which we called outlasting activities. Independently of the mechanism of epileptogenesis seizures showed some analogous characteristics: alteration of the neuronal firing pattern with increased bursts probability, a constant tendency toward generalization, faster propagation and increased synchrony over the time, and modulation by the state of vigilance (overt during SWS and completely abolished during REM sleep). Silent, hyperpolarized, states of cortical neurons favor the induction of burst firing in response to depolarizing inputs, and the postsynaptic influence of a burst is much stronger as compared to a single spike. Furthermore, we brought evidences that a particular type of neocortical neurons - fast rhythmic bursting (FRB) class - is capable to consistently respond with bursts during the hyperpolarized phase of the slow oscillation, fact that may play a very important role in both normal brain processing and in epileptogenesis. Finally, we reported a third plastic mechanism in the cortical network following seizures - a decreasing amplitude of cortically evoked excitatory post-synaptic potentials (EPSP) following seizures - which may be one of the factors responsible for the behavioral deficits observed in patients with epilepsy. We conclude that incessant transitions between active and silent states in cortico-thalamic circuits induced either by disfacilitation (sleep), cortical deafferentation (4-Hz ictal episodes) and by kindling (outlasting activities) create favorable circumstances for epileptogenesis. The increase in burst-firing, which further induce abnormally strong postsynaptic excitation, shifts the balance of excitation and inhibition toward overexcitation leading to the onset of seizures

Learning more with less data using domain-guided machine learning: the case for health data analytics

The United States is facing a shortage of neurologists with severe consequences: a) average wait-times to see neurologists are increasing, b) patients with chronic neurological disorders are unable to receive diagnosis and care in a timely fashion, and c) there is an increase in neurologist burnout leading to physical and emotional exhaustion. Present-day neurological care relies heavily on time-consuming visual review of patient data (e.g., neuroimaging and electroencephalography (EEG)), by expert neurologists who are already in short supply. As such, the healthcare system needs creative solutions that can increase the availability of neurologists to patient care. To meet this need, this dissertation develops a machine-learning (ML)-based decision support framework for expert neurologists that focuses the experts’ attention to actionable information extracted from heterogeneous patient data and reduces the need for expert visual review. Specifically, this dissertation introduces a novel ML framework known as domain-guided machine learning (DGML) and demonstrates its usefulness by improving the clinical treatments of two major neurological diseases, epilepsy and Alzheimer’s disease. In this dissertation, the applications of this framework are illustrated through several studies conducted in collaboration with the Mayo Clinic, Rochester, Minnesota. Chapters 3, 4, and 5 describe the application of DGML to model the transient abnormal discharges in the brain activity of epilepsy patients. These studies utilized the intracranial EEG data collected from epilepsy patients to delineate seizure generating brain regions without observing actual seizures; whereas, Chapters 6, 7, 8, and 9 describe the application of DGML to model the subtle but permanent changes in brain function and anatomy, and thereby enable the early detection of chronic epilepsy and Alzheimer’s disease. These studies utilized the scalp EEG data of epilepsy patients and two population-level multimodal imaging datasets collected from elderly individuals

Epilepsy

Epilepsy is the most common neurological disorder globally, affecting approximately 50 million people of all ages. It is one of the oldest diseases described in literature from remote ancient civilizations 2000-3000 years ago. Despite its long history and wide spread, epilepsy is still surrounded by myth and prejudice, which can only be overcome with great difficulty. The term epilepsy is derived from the Greek verb epilambanein, which by itself means to be seized and to be overwhelmed by surprise or attack. Therefore, epilepsy is a condition of getting over, seized, or attacked. The twelve very interesting chapters of this book cover various aspects of epileptology from the history and milestones of epilepsy as a disease entity, to the most recent advances in understanding and diagnosing epilepsy

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Brain Injury

The present two volume book "Brain Injury" is distinctive in its presentation and includes a wealth of updated information on many aspects in the field of brain injury. The Book is devoted to the pathogenesis of brain injury, concepts in cerebral blood flow and metabolism, investigative approaches and monitoring of brain injured, different protective mechanisms and recovery and management approach to these individuals, functional and endocrine aspects of brain injuries, approaches to rehabilitation of brain injured and preventive aspects of traumatic brain injuries. The collective contribution from experts in brain injury research area would be successfully conveyed to the readers and readers will find this book to be a valuable guide to further develop their understanding about brain injury

Single session neurofeedback treatment alters theta/beta-1 and theta/alpha ratios but not sufficient to induce clinical enhancement in attention

Background and Aims: Neurofeedback treatment (NFT) may enhance attention performance in healthy individuals. This study investigated the effects of single session NFT on attention in healthy individuals performing a visual attention task. Methods: This was an open-label single-blinded trial conducted on 14 healthy university students (n=14; mean=23.35±0.58 years) of a major medical university in Iran. The subjects underwent a single session NFT while performing attentional network task (ANT). The NFT protocol was theta suppression/beta-1enhancement applied at Cz for 20 min. Before and immediately after NFT, EEG signals were recorded in subjects while performing ANT. EEGs were recorded using a 19 channel device and 10-20 placement protocol. Results: The single session NFT increased the theta/beta-1 ratio in most of the electrode sites and the increase was statistically significant compared to the pre-intervention in the T6 site (p=0.011). The ratio decreased in just three sites of C3, Fz, and Cz, of them Fz showed a significant reduction (p=0.026). Contrary, the theta/alpha ratio decreased in most of the electrodes where the reductions were statistically significant in P3, P4, Cz, Pz (p<0.05), and C3 (p<0.01). The F7, F8, T3, and T4 showed no significant increased theta/alpha ratio. The central, temporal and occipital regions were involved in the NFT induced changes. Single NFT did not significantly change alerting, executive, or orienting networks of ANT. Conclusions: Theta/beta-1 and theta/alpha ratios can be reliably used to assess NFT induced attention enhancement. However, single NFT did not induce clinical outcomes and repeated sessions seem necessary to modulate alerting, executive, or orienting networks of ANT

Alpha asymmetry index of prefrontal and temporal regions predicts treatment response to repetitive transcranial magnetic stimulation

Aims: Electroencephalography (EEG) measures could be a potential markers for prediction of repetitive transcranial magnetic stimulation (rTMS) outcomes in depression. The aim of this study was to investigate the value of alpha asymmetric index (AAI) in predicting treatment response in two different protocols of rTMS in patients with intractable major depression. Methods: Patients with intractable major depression (n=34) were divided into two treatment groups underwent two different rTMS protocols. The first group received ten sessions 20 Hz rTMS The second group received 10 Hz rTMS . The EEGs were recorded in all subjects pre- and post-intervention using a 19-channel, 10-20 electrodes placement protocol. Hamilton depression rating scale-17 items (HDRS-17) was used to divide the patients into responders and non-responder. The AAI in prefrontal (Fp1-Fp2), frontal (F3-F4 and F7-F8), and temporal (T3-T4) regions were calculated and compared between pre- and post-intervention in each group and between the responder and non-responder groups. Results: In the 20 Hz rTMS group responders, 6 patients responded to the treatment, whereas 10 Hz rTMS showed 8 responders. In the responders of 20 Hz rTMS, the AAI at Fp1-Fp2, F3-F4, and T3-T4significantly decreased after the intervention (P=0.011, P=0.042, and P=0.035). The responders of 10 Hz rTMS showed significant reduction in the AAI at Fp1-Fp2 and T3-T4 regions after intervention (P= 0.023 and P=0.044). Conclusions: It seems AAI at prefrontal and temporal region scould be used for prediction of treatment response, regardless of rTMS protocol