Impaired brain-heart axis in focal epilepsy: Alterations in information flow and implications for seizure dynamics.

This study delves into functional brain-heart interplay (BHI) dynamics during interictal periods before and after seizure events in focal epilepsy. Our analysis focuses on elucidating the causal interaction between cortical and autonomic nervous system (ANS) oscillations, employing electroencephalography and heart rate variability series. The dataset for this investigation comprises 47 seizure events from 14 independent subjects, obtained from the publicly available Siena Dataset. Our findings reveal an impaired brain-heart axis especially in the heart-to-brain functional direction. This is particularly evident in bottom-up oscillations originating from sympathovagal activity during the transition between preictal and postictal periods. These results indicate a pivotal role of the ANS in epilepsy dynamics. Notably, the brain-to-heart information flow targeting cardiac oscillations in the low-frequency band does not display significant changes. However, there are noteworthy changes in cortical oscillations, primarily originating in central regions, influencing heartbeat oscillations in the high-frequency band. Our study conceptualizes seizures as a state of hyperexcitability and a network disease affecting both cortical and peripheral neural dynamics. Our results pave the way for a deeper understanding of BHI in epilepsy, which holds promise for the development of advanced diagnostic and therapeutic approaches also based on bodily neural activity for individuals living with epilepsy.

This study focuses on brain-heart interplay (BHI) during pre- and postictal periods surrounding seizures. Employing multichannel EEG and heart rate variability data from subjects with focal epilepsy, our analysis reveals a disrupted brain-heart axis dynamic, particularly in the heart-to-brain direction. Notably, sympathovagal activity alterations during preictal to postictal transitions underscore the autonomic nervous system's pivotal role in epilepsy dynamics. While brain-to-heart information flow targeting low-frequency band cardiac oscillations remains stable, significant changes occur in cortical oscillations, predominantly in central regions, influencing high-frequeny-band heartbeat oscillations, that is, vagal activity. Viewing seizures as states of hyperexcitability and confirming focal epilepsy as a network disease affecting both central and peripheral neural dynamics, our study enhances understanding of BHI in epilepsy. These findings offer potential for advanced diagnostic and therapeutic approaches grounded in bodily neural activity for individuals with epilepsy.

Electroencephalographic Correlates in Synthetic and Real Emotional Face Stimulation.

This work reports on physiological electroencephalographic (EEG) correlates in cognitive and emotional processes within the discrimination between synthetic and real faces visual stimuli. Human perception of manipulated data has been addressed in the literature from several perspectives. Researchers have investigated how the use of deep fakes alters people's ability in face-processing tasks, such as face recognition. Although recent studies showed that humans, on average, are still able to correctly recognize synthetic faces, this study investigates whether those findings still hold considering the latest advancements in AI-based, synthetic image creation. Specifically, 18-channels EEG signals from 21 healthy subjects were analyzed during a visual experiment where synthetic and actual emotional stimuli were administered. According to recent literature, participants were able to discriminate the real faces from the synthetic ones, by correctly classifying about 77% of all images. Preliminary encouraging results showed statistical significant differences in brain activation in both stimuli (synthetic and real) classification and emotional response.

Real and Deepfake Face Recognition: An EEG Study on Cognitive and Emotive Implications.

The human brain's role in face processing (FP) and decision making for social interactions depends on recognizing faces accurately. However, the prevalence of deepfakes, AI-generated images, poses challenges in discerning real from synthetic identities. This study investigated healthy individuals' cognitive and emotional engagement in a visual discrimination task involving real and deepfake human faces expressing positive, negative, or neutral emotions. Electroencephalographic (EEG) data were collected from 23 healthy participants using a 21-channel dry-EEG headset; power spectrum and event-related potential (ERP) analyses were performed. Results revealed statistically significant activations in specific brain areas depending on the authenticity and emotional content of the stimuli. Power spectrum analysis highlighted a right-hemisphere predominance in theta, alpha, high-beta, and gamma bands for real faces, while deepfakes mainly affected the frontal and occipital areas in the delta band. ERP analysis hinted at the possibility of discriminating between real and synthetic faces, as N250 (200-300 ms after stimulus onset) peak latency decreased when observing real faces in the right frontal (LF) and left temporo-occipital (LTO) areas, but also within emotions, as P100 (90-140 ms) peak amplitude was found higher in the right temporo-occipital (RTO) area for happy faces with respect to neutral and sad ones.

Interaction of Virus in Cancer Patients: A Theoretical Dynamic Model.

This study reports on a phase-space analysis of a mathematical model of tumor growth with the interaction between virus and immune response. In this study, a mathematical determination was attempted to demonstrate the relationship between uninfected cells, infected cells, effector immune cells, and free viruses using a dynamic model. We revealed the stability analysis of the system and the Lyapunov stability of the equilibrium points. Moreover, all endemic equilibrium point models are derived. We investigated the stability behavior and the range of attraction sets of the nonlinear systems concerning our model. Furthermore, a global stability analysis is proved either in the construction of a Lyapunov function showing the validity of the concerned disease-free equilibria or in endemic equilibria discussed by the model. Finally, a simulated solution is achieved and the relationship between cancer cells and other cells is drawn.

Heart and brain: Change in cardiac entropy is related to lateralised visual inspection in horses.

Cerebral lateralisation is the tendency for an individual to preferentially use one side of their brain and is apparent in the biased use of paired sensory organs. Horses vary in eye use when viewing a novel stimulus which may be due to different physiological reactions. To understand the interplay between physiology and lateralisation, we presented a novel object (an inflated balloon) to 20 horses while electrocardiogram traces were collected. We measured the amount of time each horse looked at the balloon with each eye. We calculated 'sample entropy' as a measure of non-linear heart rate variability both prior to and during the stimulus presentation. A smaller drop in sample entropy values between the habituation phase and the sample presentation indicates the maintenance of a more complex signal associated with a relaxed physiological state. Horses that spent longer viewing the balloon with their left eye had a greater reduction in sample entropy, while time spend looking with the right eye was unrelated to the change in sample entropy. Therefore, the horses that exhibited a greater reduction in sample entropy tended to use their right hemisphere more, which may take precedence in emotional reactions. These results may help to explain the variation in lateralisation observed among horses.

Autonomic nervous system balance in parturient mares: Spontaneous vs induced delivery.

Delivery is not easily predictable in horses and the consequences of dystocia can be serious for both the mare and foal. An induction protocol with low doses of oxytocin has been reported as a safe procedure. This study investigates the effect of induced delivery on at-term mares' sympathetic-vagal balance. Fourteen mares were included and divided into two groups, one subjected to spontaneous delivery (SD), and one to induced delivery (ID). In both groups, an ECG was recorded using an elastic belt with integrated smart textile electrodes. The recording started before the delivery (Basal), continued close to delivery (Pre-delivery) and during delivery (Delivery), and ended after parturition (Placental expulsion). From the ECGs, Heart Rate Variability (HRV) parameters relating to time and frequency domains and non-linear analysis were extrapolated. The HRV analysis was performed both within the same group (IntraGA) and between the two groups (InterGA). In the present study, spontaneous and induced delivery did not appear to differ in autonomic nervous system functioning. In IntraGA analysis, both for SD and ID mares, delivery and placental expulsion periods were parasympathetic dominated since vagal-related HRV parameters increased. Moreover, no differences were found in InterGA comparison between SD and ID mares, except for the pre-delivery period of ID mares, during which both branches of the autonomic nervous system were activated. These results are in line with the literature on parasympathetic dominance during parturition and no change in Heart Rate Variability following exogenous oxytocin administration in parturient mares.

Eye-tracking correlates of the Implicit Association Test.

Raising awareness of environmental challenges represents an important issue for researchers and scientists. As public opinion remains ambiguous, implicit attitudes toward climate change must be investigated. A custom Single-Category Implicit Association Test, a version of the Implicit Association Test, was developed to assess climate change beliefs. It was administered to 20 subjects while eye movements were tracked using a smart glasses system. Eye gaze patterns were analysed to understand whether they could reflect implicit attitudes toward nature. Recurrence Quantification Analysis was performed to extract 13 features from the eye-tracking data, which were used to perform statistical analyses. Significant differences were found between target stimuli (words related to climate change) and bad attributes in reaction time, and between target stimuli and good attributes in diagonal length entropy, suggesting that eye-tracking may provide an alternative source of information to electroencephalography in modeling and predicting implicit attitudes.

Automatic COVID-19 severity assessment from HRV.

COVID-19 is known to be a cause of microvascular disease imputable to, for instance, the cytokine storm inflammatory response and the consequent blood coagulation. In this study, we propose a methodological approach for assessing the COVID-19 presence and severity based on Random Forest (RF) and Support Vector Machine (SVM) classifiers. Classifiers were applied to Heart Rate Variability (HRV) parameters extracted from photoplethysmographic (PPG) signals collected from healthy and COVID-19 affected subjects. The supervised classifiers were trained and tested on HRV parameters obtained from the PPG signals in a cohort of 50 healthy subjects and 93 COVID-19 affected subjects, divided into two groups, mild and moderate, based on the support of oxygen therapy and/or ventilation. The most informative feature set for every group's comparison was determined with the Least Absolute Shrinkage and Selection Operator (LASSO) technique. Both RF and SVM classifiers showed a high accuracy percentage during groups' comparisons. In particular, the RF classifier reached 94% of accuracy during the comparison between the healthy and minor severity COVID-19 group. Obtained results showed a strong capability of RF and SVM to discriminate between healthy subjects and COVID-19 patients and to differentiate the two different COVID-19 severity. The proposed method might be helpful for detecting, in a low-cost and fast fashion, the presence and severity of COVID-19 disease; moreover, these reasons make this method interesting as a starting point for future studies that aim to investigate its effectiveness as a possible screening method.

Artificial Intelligence Procedure for the Screening of Genetic Syndromes Based on Voice Characteristics.

Perceptual and statistical evidence has highlighted voice characteristics of individuals affected by genetic syndromes that differ from those of normophonic subjects. In this paper, we propose a procedure for systematically collecting such pathological voices and developing AI-based automated tools to support differential diagnosis. Guidelines on the most appropriate recording devices, vocal tasks, and acoustical parameters are provided to simplify, speed up, and make the whole procedure homogeneous and reproducible. The proposed procedure was applied to a group of 56 subjects affected by Costello syndrome (CS), Down syndrome (DS), Noonan syndrome (NS), and Smith-Magenis syndrome (SMS). The entire database was divided into three groups: pediatric subjects (PS; individuals < 12 years of age), female adults (FA), and male adults (MA). In line with the literature results, the Kruskal-Wallis test and post hoc analysis with Dunn-Bonferroni test revealed several significant differences in the acoustical features not only between healthy subjects and patients but also between syndromes within the PS, FA, and MA groups. Machine learning provided a k-nearest-neighbor classifier with 86% accuracy for the PS group, a support vector machine (SVM) model with 77% accuracy for the FA group, and an SVM model with 84% accuracy for the MA group. These preliminary results suggest that the proposed method based on acoustical analysis and AI could be useful for an effective, non-invasive automatic characterization of genetic syndromes. In addition, clinicians could benefit in the case of genetic syndromes that are extremely rare or present multiple variants and facial phenotypes.

Wearable Systems for Home Monitoring Healthcare: The Photoplethysmography Success Pros and Cons.

The widespread use of remote technology has moved medical care services into individuals' homes. In this perspective, the ubiquitous computing research proposes self-management and remote monitoring to help patients with healthcare in low-cost everyday home usage systems based on the latest technological advances in sensors, communication, and portability. This work analyzes recent publications on the paradigm of continuous monitoring through wearable and portable systems, focusing on photoplethysmography (PPG) advances and referencing the current systematic study proposed by Fine et al. The study revised the literature highlighting the pros and cons of using the PPG system for fitness, wellbeing, and medical devices. However, future works should focus on the standardization of the practical use and assessment of the quality of the PPGs' output. For clinical parameter extraction methodology in terms of biological sites of application and signal processing methods, PPG is the most convenient and widely used system potentially suitable for the decentralized paradigm of continuous monitoring healthcare concepts.

Advances in Multivariate and Multiscale Physiological Signal Analysis.

Physiological systems are characterized by complex dynamics and nonlinear behaviors due to their intricate structural organization and regulatory mechanisms [...].

Validation of a new smart textiles biotechnology for heart rate variability monitoring in sheep.

Robust Animal-Based Measures (ABMs) are fundamental in order to assess animal welfare, however in semi-extensive sheep farming systems is not easy to collect ABMs without inducing additional stress in the animals. Heart rate variability (HRV) is a non-invasive technique of assessing stress levels related to animal welfare. It is considered a sensitive indicator of the functional regulatory characteristics of the autonomic nervous system. Several studies investigated the use of HRV for welfare assessment in dairy cows while research on sheep is scarce. Moreover, assessing HRV in small ruminants at pasture is critical because of the lack of a solution adoptable for field conditions. This study aimed to evaluate if a smart textiles technology is comparable to a Standard base-apex electrocardiogram (ECG) for measuring HRV in small ruminants. Eight healthy Massese dairy sheep were recruited. Standard base-apex ECG and smart textiles technology (Smartex ECG) were simultaneously acquired for 5 min in the standing, unsedated, unclipped sheep. The ECG tracings were recorded when animals were standing quietly. The Bland-Altman test and the linear regression analysis were applied after parameter extraction in time, frequency, and non-linear methods to compare Smartex against standard base-apex ECG systems. The Bland-Altman test was applied to all HRV extracted parameters (Mean RR, pNN50, RMSSD, LF/HF, SampEn, SD1, SD2, stdRR) to evaluate the agreement between the two different instruments, and a linear regression analysis was performed to evaluate the relationship between the two methods. The smart textiles biotechnology was simple to wear and clean. It can be worn without using glue and without shaving the sheep's wool, limiting animal handling and stress. Bland Altman test reported a robust agreement between the two systems. In fact, the regression analysis of HRV parameters showed that half of the parameters recorded had an R2 coefficient >0.75. Results also showed a very small reproducibility coefficient that indicated that the two methods were really close to each other. Smartex textiles technology can be used for HRV evaluation in sheep species as a potential ABM for animal welfare assessment.

Cardiovascular Dynamics in COVID-19: A Heart Rate Variability Investigation.

COVID-19 is known to be a cause of microvascular disease due, for example, to the cytokine storm inflammatory response and the result of blood coagulation. This study reports an investigation on Heart Rate Variability (HRV) extracted from photoplethysmography (PPG) signals measured from healthy subjects and COVID-19 affected patients. We aimed to determine a statistical difference between HRV parameters among subjects' groups. Specifically, statistical analysis through Mann-Whitney U Test (MWUT) was applied to compare 42 dif-ferent parameters extracted from PPG signals of 143 subjects: 50 healthy subjects (i.e. group 0) and 93 affected from COVID-19 patients stratified through increasing COVID severity index (i.e. groups 1 and 2). Results showed significant statistical differences between groups in several HRV parameters. In particular, Multiscale Entropy (MSE) analysis provided the master key in patient stratification assessment. In fact, MSE11, MSE12, MSE15, MSE16, MSE17, MSE18, MSE19 and MSE20 keep statistical significant difference during all the comparisons between healthy subjects and patients from all the pathological groups. Our preliminary results suggest that it could be possible to distinguish between healthy and COVID-19 affected subjects based on cardiovascular dynamics. This study opens to future evaluations in using machine learning models for automatic decision-makers to distinguish between healthy and COVID-19 subjects, as well as within COVID-19 severity levels. Clinical Relevance - This establishes the possibility to distin-guish healthy subjects from COVID-19 affected patients basing on HRV parameters monitored non invasively by PPG.

Aggregate Channel Features for newborn face detection in Neonatal Intensive Care Units.

An efficient face detector could be very helpful to point out possible neurological dysfunctions such as seizure events in Neonatal Intensive Care Units. However, its development is still challenging because large public datasets of newborns' faces are missing. Over the years several studies introduced semi-automatic approaches. This study proposes a fully automated face detector for newborns in Neonatal Intensive Care Units, based on the Aggregate Channel Feature algorithm. The developed method is tested on a dataset of video recordings from 42 full-term newborns collected at the Neuro-physiopathology and Neonatology Clinical Units, AOU Careggi, Firenze, Italy. The proposed system showed promising results, giving (mean ± standard error): log-Average Miss Rate = 0.47 ± 0.05 and Average Precision Recall = 0.61 ± 0.05. Moreover, achieved results highlighted interesting differences between newborns without seizures, newborns with electro-clinical seizures, and newborns with electrographic-only seizures. For both metrics statistically significant differences were found between patients with electro-clinical seizures and the other two groups. Clinical Relevance- The proposed method, based on quantitative physio-pathological features of facial movements, is of clinical relevance as it could speed up pain or seizure assessment of newborns in Neonatal Intensive Care Units.

Analysis of Brain-Heart Interactions in newborns with and without seizures using the Convergent Cross Mapping approach.

In the last years, the characterization of brain-heart interactions (BHIs) in epilepsy has gained great interest. For some specific seizures there is still a lack of information about the mechanisms occurring during or close to ictal events between the central nervous system (CNS) and the autonomic nervous system (ANS). This is the case for neonatal seizures, one of the most common neurological emergencies in the first days of life. This paper evaluates possible differences in BHIs between newborns with seizures and seizure-free ones. We applied convergent cross mapping approaches to a cohort of 52 newborns from a public dataset. Preliminary results show that newborns with seizures have a lower degree of interaction between the CNS and the ANS than seizure-free ones (Mann-Whitney test: p-value <0.05). These results are of clinical relevance for future BHI-based approaches to better understand the neural mechanisms behind neonatal seizures. Clinical Relevance- The study of BHIs in newborns with seizures might be helpful to better characterize the disorder or the aetiologies behind ictal events. Moreover, BHI approaches may confirm the involvement of the ANS during or close to a neonatal seizure event.

Heart Rate Variability Analysis for Seizure Detection in Neonatal Intensive Care Units.

In Neonatal Intensive Care Units (NICUs), the early detection of neonatal seizures is of utmost importance for a timely clinical intervention. Over the years, several neonatal seizure detection systems were proposed to detect neonatal seizures automatically and speed up seizure diagnosis, most based on the EEG signal analysis. Recently, research has focused on other possible seizure markers, such as electrocardiography (ECG). This work proposes an ECG-based NSD system to investigate the usefulness of heart rate variability (HRV) analysis to detect neonatal seizures in the NICUs. HRV analysis is performed considering time-domain, frequency-domain, entropy and multiscale entropy features. The performance is evaluated on a dataset of ECG signals from 51 full-term babies, 29 seizure-free. The proposed system gives results comparable to those reported in the literature: Area Under the Receiver Operating Characteristic Curve = 62%, Sensitivity = 47%, Specificity = 67%. Moreover, the system's performance is evaluated in a real clinical environment, inevitably affected by several artefacts. To the best of our knowledge, our study proposes for the first time a multi-feature ECG-based NSD system that also offers a comparative analysis between babies suffering from seizures and seizure-free ones.

HRV analysis: a non-invasive approach to discriminate between newborns with and without seizures.

Early neonatal seizures detection is one of the most challenging issues in Neonatal Intensive Care Units. Several EEG-based Neonatal Seizure Detectors were proposed to support the clinical staff. However, less invasive and more easily interpretable methods than EEG are still missing. In this work, we investigated if Heart Rate Variability analysis and related measures as input features of supervised classifiers could be a valid support for discriminating between newborns with seizures and seizure-free ones. The proposed methods were validated on 52 subjects (33 with seizures and 19 seizure-free) of a public dataset collected at the Helsinki University Hospital. Encouraging results are achieved using a Linear Support Vector Machine, obtaining about 87% Area Under ROC Curve. This suggests that Heart Rate Variability analysis might be a non-invasive pre-screening tool to identify newborns with seizures.Clinical Relevance- Heart Rate Variability analysis for detecting newborns with seizures in NICUs could speed up the diagnosis process and appropriate treatments for a better neurodevelopmental outcome of the infant.

A Generalized Linear Model for an ECG-based Neonatal Seizure Detector.

Seizures represent one of the most challenging issues of the neonatal period's neurological emergency. Due to the heterogeneity of etiologies and clinical characteristics, seizures recognition is tricky and time-consuming. Currently, the gold standard for seizure diagnosis is Electroencephalography (EEG), whose correct interpretation requires a highly specialized team. Thus, to speed up and facilitate the detection of ictal events, several EEG-based Neonatal Seizure Detectors (NSDs) have been proposed in the literature. Research is currently exploiting more simple and less invasive approaches, such as Electrocardiography (ECG). This work aims at developing an ECG-based NSD using a Generalized Linear Model with features extracted from Heart Rate Variability (HRV) measures as input. The method is validated on a public dataset of 52 subjects (33 with seizures and 19 seizure-free). Achieved encouraging results show 69% Concatenated Area Under the ROC Curve (AUCcc) for the automatic detection of windows with seizure events, confirming that HRV features can be useful to catch the cardio-regulatory system alterations due to neonatal seizure events, particularly those related to Hypoxic-Ischaemic Encephalopathies. Thus, results suggest the use of ECG-based NSDs in clinical practice, especially when a timely diagnosis is needed and EEG technologies are not readily available.Clinical Relevance- An ECG-based Neonatal Seizure Detector could be a valid support to speed up the diagnosis of neonatal seizures, especially when EEG technologies for infants' neurological assessment are not readily available.

Multiscale Entropy Analysis of Heart Rate Variability in Neonatal Patients with and without Seizures.

The complex physiological dynamics of neonatal seizures make their detection challenging. A timely diagnosis and treatment, especially in intensive care units, are essential for a better prognosis and the mitigation of possible adverse effects on the newborn's neurodevelopment. In the literature, several electroencephalographic (EEG) studies have been proposed for a parametric characterization of seizures or their detection by artificial intelligence techniques. At the same time, other sources than EEG, such as electrocardiography, have been investigated to evaluate the possible impact of neonatal seizures on the cardio-regulatory system. Heart rate variability (HRV) analysis is attracting great interest as a valuable tool in newborns applications, especially where EEG technologies are not easily available. This study investigated whether multiscale HRV entropy indexes could detect abnormal heart rate dynamics in newborns with seizures, especially during ictal events. Furthermore, entropy measures were analyzed to discriminate between newborns with seizures and seizure-free ones. A cohort of 52 patients (33 with seizures) from the Helsinki University Hospital public dataset has been evaluated. Multiscale sample and fuzzy entropy showed significant differences between the two groups (p-value < 0.05, Bonferroni multiple-comparison post hoc correction). Moreover, interictal activity showed significant differences between seizure and seizure-free patients (Mann-Whitney Test: p-value < 0.05). Therefore, our findings suggest that HRV multiscale entropy analysis could be a valuable pre-screening tool for the timely detection of seizure events in newborns.

Smart textiles biotechnology for electrocardiogram monitoring in horses during exercise on treadmill: Validation tests.

BACKGROUND: There are several bioengineering solutions aimed at improving human health and welfare. Smart electrodes based on textile substrates have met the growing demand for comfort, reliability, and robustness when acquiring physiological signals. OBJECTIVES: Given the importance of good quality electrocardiograms (ECG) in equine sports medicine, this study focuses on the validation of smart textile electrodes to acquire ECG signals in horses during treadmill exercise. STUDY DESIGN: The performance of the smart textile electrodes is compared with standard silver/silver chloride (Ag/AgCl) electrodes in terms of signal quality. METHODS: Five healthy Standardbred mares were fitted with two identical electronic systems for the simultaneous recording of ECGs during a standardised exercise test (SET) on a treadmill. One system was equipped with smart textile electrodes, whereas the second was equipped with standard Ag/AgCl electrodes. The Ag/AgCl electrodes were positioned on shaved skin with self-adhesive pads, and without (SET1) or with glue (SET2). The textile electrodes were positioned without shaving the skin. The Kurtosis (k) value for each ECG trace recorded was calculated as an index of ECG signal quality. RESULTS: For the textile electrodes, k values were higher, and closer to ideal compared to Ag/AgCl electrodes. The median values of the Signal Quality Indexes (kSQI) were higher for textile compared to Ag/AgCl electrodes. These differences were significant in SET 2 (P < .001), but not in SET 1 (P = .08). MAIN LIMITATIONS: This study was limited to treadmill exercise that did not include a rider or harness. CONCLUSIONS: During treadmill exercise, textile electrodes are a practical solution for collecting good quality ECG traces.