Evaluating the Window Size's Role in Automatic EEG Epilepsy Detection.

Electroencephalography is one of the most commonly used methods for extracting information about the brain's condition and can be used for diagnosing epilepsy. The EEG signal's wave shape contains vital information about the brain's state, which can be challenging to analyse and interpret by a human observer. Moreover, the characteristic waveforms of epilepsy (sharp waves, spikes) can occur randomly through time. Considering all the above reasons, automatic EEG signal extraction and analysis using computers can significantly impact the successful diagnosis of epilepsy. This research explores the impact of different window sizes on EEG signals' classification accuracy using four machine learning classifiers. The machine learning methods included a neural network with ten hidden nodes trained using three different training algorithms and the k-nearest neighbours classifier. The neural network training methods included the Broyden-Fletcher-Goldfarb-Shanno algorithm, the multistart method for global optimization problems, and a genetic algorithm. The current research utilized the University of Bonn dataset containing EEG data, divided into epochs having 50% overlap and window lengths ranging from 1 to 24 s. Then, statistical and spectral features were extracted and used to train the above four classifiers. The outcome from the above experiments showed that large window sizes with a length of about 21 s could positively impact the classification accuracy between the compared methods.

Assessing Electroencephalography as a Stress Indicator: A VR High-Altitude Scenario Monitored through EEG and ECG.

Over the last decade, virtual reality (VR) has become an increasingly accessible commodity. Head-mounted display (HMD) immersive technologies allow researchers to simulate experimental scenarios that would be unfeasible or risky in real life. An example is extreme heights exposure simulations, which can be utilized in research on stress system mobilization. Until recently, electroencephalography (EEG)-related research was focused on mental stress prompted by social or mathematical challenges, with only a few studies employing HMD VR techniques to induce stress. In this study, we combine a state-of-the-art EEG wearable device and an electrocardiography (ECG) sensor with a VR headset to provoke stress in a high-altitude scenarios while monitoring EEG and ECG biomarkers in real time. A robust pipeline for signal clearing is implemented to preprocess the noise-infiltrated (due to movement) EEG data. Statistical and correlation analysis is employed to explore the relationship between these biomarkers with stress. The participant pool is divided into two groups based on their heart rate increase, where statistically important EEG biomarker differences emerged between them. Finally, the occipital-region band power changes and occipital asymmetry alterations were found to be associated with height-related stress and brain activation in beta and gamma bands, which correlates with the results of the self-reported Perceived Stress Scale questionnaire.

IoT Micro-Blockchain Fundamentals.

In this paper we investigate the essential minimum functionality of the autonomous blockchain, and the minimum hardware and software required to support it in the micro-scale in the IoT world. The application of deep-blockchain operation in the lower-level activity of the IoT ecosystem, is expected to bring profound clarity and constitutes a unique challenge. Setting up and operating bit-level blockchain mechanisms on minimal IoT elements like smart switches and active sensors, mandates pushing blockchain engineering to the limits. "How deep can blockchain actually go?" "Which is the minimum Thing of the IoT world that can actually deliver autonomous blockchain functionality?" To answer, an experiment based on IoT micro-controllers was set. The "Witness Protocol" was defined to set the minimum essential micro-blockchain functionality. The protocol was developed and installed on a peer, ad-hoc, autonomous network of casual, real-life IoT micro-devices. The setup was tested, benchmarked, and evaluated in terms of computational needs, efficiency, and collective resistance against malicious attacks. The leading considerations are highlighted, and the results of the experiment are presented. Findings are intriguing and prove that fully autonomous, private micro-blockchain networks are absolutely feasible in the smart dust world, utilizing the capacities of the existing low-end IoT devices.

EEG-Based Eye Movement Recognition Using Brain-Computer Interface and Random Forests.

Discrimination of eye movements and visual states is a flourishing field of research and there is an urgent need for non-manual EEG-based wheelchair control and navigation systems. This paper presents a novel system that utilizes a brain-computer interface (BCI) to capture electroencephalographic (EEG) signals from human subjects while eye movement and subsequently classify them into six categories by applying a random forests (RF) classification algorithm. RF is an ensemble learning method that constructs a series of decision trees where each tree gives a class prediction, and the class with the highest number of class predictions becomes the model's prediction. The categories of the proposed random forests brain-computer interface (RF-BCI) are defined according to the position of the subject's eyes: open, closed, left, right, up, and down. The purpose of RF-BCI is to be utilized as an EEG-based control system for driving an electromechanical wheelchair (rehabilitation device). The proposed approach has been tested using a dataset containing 219 records taken from 10 different patients. The BCI implemented the EPOC Flex head cap system, which includes 32 saline felt sensors for capturing the subjects' EEG signals. Each sensor caught four different brain waves (delta, theta, alpha, and beta) per second. Then, these signals were split in 4-second windows resulting in 512 samples per record and the band energy was extracted for each EEG rhythm. The proposed system was compared with naïve Bayes, Bayes Network, k-nearest neighbors (K-NN), multilayer perceptron (MLP), support vector machine (SVM), J48-C4.5 decision tree, and Bagging classification algorithms. The experimental results showed that the RF algorithm outperformed compared to the other approaches and high levels of accuracy (85.39%) for a 6-class classification are obtained. This method exploits high spatial information acquired from the Emotiv EPOC Flex wearable EEG recording device and examines successfully the potential of this device to be used for BCI wheelchair technology.

High-Throughput, Machine Learning-Based Quantification of Steatosis, Inflammation, Ballooning, and Fibrosis in Biopsies From Patients With Nonalcoholic Fatty Liver Disease.

BACKGROUND & AIMS: Liver biopsy is the reference standard for staging and grading nonalcoholic fatty liver disease (NAFLD), but histologic scoring systems are semiquantitative with marked interobserver and intraobserver variation. We used machine learning to develop fully automated software for quantification of steatosis, inflammation, ballooning, and fibrosis in biopsy specimens from patients with NAFLD and validated the technology in a separate group of patients. METHODS: We collected data from 246 consecutive patients with biopsy-proven NAFLD and followed up in London from January 2010 through December 2016. Biopsy specimens from the first 100 patients were used to derive the algorithm and biopsy specimens from the following 146 were used to validate it. Biopsy specimens were scored independently by pathologists using the Nonalcoholic Steatohepatitis Clinical Research Network criteria and digitalized. Areas of steatosis, inflammation, ballooning, and fibrosis were annotated on biopsy specimens by 2 hepatobiliary histopathologists to facilitate machine learning. Images of biopsies from the derivation and validation sets then were analyzed by the algorithm to compute percentages of fat, inflammation, ballooning, and fibrosis, as well as the collagen proportionate area, and compared with findings from pathologists' manual annotations and conventional scoring systems. RESULTS: In the derivation group, results from manual annotation and the software had an interclass correlation coefficient (ICC) of 0.97 for steatosis (95% CI, 0.95-0.99; P < .001); ICC of 0.96 for inflammation (95% CI, 0.9-0.98; P < .001); ICC of 0.94 for ballooning (95% CI, 0.87-0.98; P < .001); and ICC of 0.92 for fibrosis (95% CI, 0.88-0.96; P = .001). Percentages of fat, inflammation, ballooning, and the collagen proportionate area from the derivation group were confirmed in the validation cohort. The software identified histologic features of NAFLD with levels of interobserver and intraobserver agreement ranging from 0.95 to 0.99; this value was higher than that of semiquantitative scoring systems, which ranged from 0.58 to 0.88. In a subgroup of paired liver biopsy specimens, quantitative analysis was more sensitive in detecting differences compared with the nonalcoholic steatohepatitis Clinical Research Network scoring system. CONCLUSIONS: We used machine learning to develop software to rapidly and objectively analyze liver biopsy specimens for histologic features of NAFLD. The results from the software correlate with those from histopathologists, with high levels of interobserver and intraobserver agreement. Findings were validated in a separate group of patients. This tool might be used for objective assessment of response to therapy for NAFLD in practice and clinical trials.

Improving the Accuracy of Low-Cost Sensor Measurements for Freezer Automation.

In this work, a regression method is implemented on a low-cost digital temperature sensor to improve the sensor's accuracy; thus, following the EN12830 European standard. This standard defines that the maximum acceptable error regarding temperature monitoring devices should not exceed 1 °C for the refrigeration and freezer areas. The purpose of the proposed method is to improve the accuracy of a low-cost digital temperature sensor by correcting its nonlinear response using simple linear regression (SLR). In the experimental part of this study, the proposed method's outcome (in a custom created dataset containing values taken from a refrigerator) is compared against the values taken from a sensor complying with the EN12830 standard. The experimental results confirmed that the proposed method reduced the mean absolute error (MAE) by 82% for the refrigeration area and 69% for the freezer area-resulting in the accuracy improvement of the low-cost digital temperature sensor. Moreover, it managed to achieve a lower generalization error on the test set when compared to three other machine learning algorithms (SVM, B-ELM, and OS-ELM).

Sex hormone levels in drug-naïve, first-episode patients with psychosis.

Aim: Sex differences have long been reported in schizophrenia leading to the hypothesis that sex hormones may be implicated in the pathophysiology of the disorder. We assessed gonadal hormones during the fasted state in drug-naïve patients with psychosis.Method: Fasting serum concentrations of follicular-stimulating hormone (FSH) and luteinizing hormone (LH), testosterone, free-testosterone, Sex Hormone Binding Globulin (SHBG) and oestradiol (E2) were compared between a group of 55 newly diagnosed, drug-naïve, first-episode men with psychosis and a group of 55 healthy controls, matched for age, smoking status and BMI. Testosterone, free-testosterone and SHBG were compared between a group of 32 drug-naïve, first-episode females with psychosis and a group of 32 healthy controls matched for age, smoking status and BMI.Results: Testosterone and free-testosterone levels were significantly lower in the patients' group and SHBG levels significantly higher in the patients' group compared to those in healthy controls. The two female groups had similar values in the hormones which were measured.Conclusion: Our findings provide evidence of lower testosterone and free-testosterone levels and increased SHBG levels in drug-naïve, first-episode males with psychosis.KEY POINTSReduced testosterone and free-testosterone levels in drug-naive, first-episode males with psychosis.Increased SHBG levels in drug-naive first-episode males with psychosis.No difference in FSH, LH and E2 levels between drug-naive first episode males with psychosis and controls.No difference in testosterone, free-testosterone and SHBG levels between drug-naive, first-episode women with psychosis and controls.

Prolactin levels in drug-naïve patients with schizophrenia and other psychotic disorders.

OBJECTIVE: Hyperprolactinaemia as a side effect of dopamine receptor blockers is common in patients with schizophrenia and other psychotic disorders and may lead to amenorrhoea, galactorrhoea, hypogonadism, subfertility and osteoporosis. The aim of our study was to determine whether hyperprolactinaemia occurs also in patients with schizophrenia and other psychotic disorders prior to any antipsychotic treatment. METHODS: Serum prolactin, thyroid-stimulating hormone (TSH), triiodothyronine (T3), free tetraiodothyronine (FT4) and cortisol levels were measured in 40 newly diagnosed, drug naïve, patients with schizophrenia and other psychotic disorders and in 40 age and gender matched healthy subjects. RESULTS: The median prolactin value was 12.5 ng/ml (range: 2-38 ng/ml) for patients and 8.6 ng/ml (range: 4-17.6 ng/ml) for healthy subjects (p = 0.011). Patients had lower levels of T3 compared to healthy controls (mean: 1.08 ng/ml, SD: 0.16 vs. 1.18 ng/ml, 0.18, respectively; p = 0.008). Serum TSH, FT4 and cortisol levels were similar between the two groups. Multiple regression analysis revealed that the difference in serum prolactin values was independent of thyroid function (TSH, FT4, T3) and serum cortisol levels. CONCLUSIONS: A higher serum prolactin level was found in drug naïve, newly diagnosed patients with schizophrenia and other psychotic disorders compared to healthy controls, prior to starting any antipsychotic treatment.

Wearability assessment of a wearable system for Parkinson's disease remote monitoring based on a body area network of sensors.

Wearable technologies for health monitoring have become a reality in the last few years. So far, most research studies have focused on assessments of the technical performance of these systems, as well as the validation of the clinical outcomes. Nevertheless, the success in the acceptance of these solutions depends not only on the technical and clinical effectiveness, but on the final user acceptance. In this work the compliance of a telehealth system for the remote monitoring of Parkinson's disease (PD) patients is presented with testing in 32 PD patients. This system, called PERFORM, is based on a Body Area Network (BAN) of sensors which has already been validated both from the technical and clinical point for view. Diverse methodologies (REBA, Borg and CRS scales in combination with a body map) are employed to study the comfort, biomechanical and physiological effects of the system. The test results allow us to conclude that the acceptance of this system is satisfactory with all the levels of effect on each component scoring in the lowest ranges. This study also provided useful insights and guidelines to lead to redesign of the system to improve patient compliance.

PERFORM: a system for monitoring, assessment and management of patients with Parkinson's disease.

In this paper, we describe the PERFORM system for the continuous remote monitoring and management of Parkinson's disease (PD) patients. The PERFORM system is an intelligent closed-loop system that seamlessly integrates a wide range of wearable sensors constantly monitoring several motor signals of the PD patients. Data acquired are pre-processed by advanced knowledge processing methods, integrated by fusion algorithms to allow health professionals to remotely monitor the overall status of the patients, adjust medication schedules and personalize treatment. The information collected by the sensors (accelerometers and gyroscopes) is processed by several classifiers. As a result, it is possible to evaluate and quantify the PD motor symptoms related to end of dose deterioration (tremor, bradykinesia, freezing of gait (FoG)) as well as those related to over-dose concentration (Levodopa-induced dyskinesia (LID)). Based on this information, together with information derived from tests performed with a virtual reality glove and information about the medication and food intake, a patient specific profile can be built. In addition, the patient specific profile with his evaluation during the last week and last month, is compared to understand whether his status is stable, improving or worsening. Based on that, the system analyses whether a medication change is needed--always under medical supervision--and in this case, information about the medication change proposal is sent to the patient. The performance of the system has been evaluated in real life conditions, the accuracy and acceptability of the system by the PD patients and healthcare professionals has been tested, and a comparison with the standard routine clinical evaluation done by the PD patients' physician has been carried out. The PERFORM system is used by the PD patients and in a simple and safe non-invasive way for long-term record of their motor status, thus offering to the clinician a precise, long-term and objective view of patient's motor status and drug/food intake. Thus, with the PERFORM system the clinician can remotely receive precise information for the PD patient's status on previous days and define the optimal therapeutical treatment.

A Mathematical Model of Pressure Ulcer Formation to Facilitate Prevention and Management.

Pressure ulcers are a frequent issue involving localized damage to the skin and underlying tissues, commonly arising from prolonged hospitalization and immobilization. This paper introduces a mathematical model designed to elucidate the mechanics behind pressure ulcer formation, aiming to predict its occurrence and assist in its prevention. Utilizing differential geometry and elasticity theory, the model represents human skin and simulates its deformation under pressure. Additionally, a system of ordinary differential equations is employed to predict the outcomes of these deformations, estimating the cellular death rate in skin tissues and underlying layers. The model also incorporates changes in blood flow resulting from alterations in skin geometry. This comprehensive approach provides new insights into the optimal bed surfaces required to prevent pressure ulcers and offers a general predictive method to aid healthcare personnel in making informed decisions for at-risk patients. Compared to existing models in the literature, our model delivers a more thorough prediction method that aligns well with current data. It can forecast the time required for an immobilized individual to develop an ulcer in various body parts, considering different initial health conditions and treatment strategies.

Effects of antipsychotic medications in glucose and lipid metabolism at the fasted state in drug-naïve first episode patients with psychosis after six months and three years of treatment.

Diabetes and dyslipidemia are common in patients with psychosis and may be related to adverse effects of antipsychotic medications. Metabolic disturbances in first-episode patients with psychosis are common, even prior to any antipsychotic treatment, and antipsychotic medications are implicated in the development of metabolic syndrome, at least in the long run. We therefore aimed to follow a group of drug-naïve, first-episode patients with psychosis at different time points (baseline, six months, and 36 months after the initiation of antipsychotic treatment) in order to evaluate the progression of metabolic abnormalities after antipsychotic therapy and the time-course of their onset. We assessed glucose and lipid metabolism during the fasted state in 54 drug-naïve patients with first-episode psychosis (FEP) before the initiation of any antipsychotic treatment and compared them with matched controls. The same parameters were assessed in the patient group (n=54) after six months of antipsychotic treatment and in a subgroup of patients (n=39) after three years of continuous and stable treatment in comparison to baseline. Measurements were obtained for fasting serum concentrations of total cholesterol, triglycerides, high density lipoprotein (HDL), glucose, insulin, connecting peptide (C-peptide), homeostatic model assessment index (HOMA-IR), glycated hemoglobin (HbA1c) and body mass index (BMI). Insulin, C-peptide, triglyceride levels, and HOMA-IR index were significantly higher compared to controls. Total cholesterol, triglyceride levels and BMI, increased significantly in the patient group after six months of antipsychotic treatment. After three years of continuous antipsychotic treatment, we found statistically significant increases in fasting glucose, insulin, total cholesterol, triglyceride levels, HbA1c, HOMA-IR index, and BMI compared to baseline. In conclusion, FEP patients developed significant increases in BMI and serum lipid levels as soon as six months after antipsychotic treatment. These metabolic abnormalities persisted following 36 months of treatment and in addition, increases in fasting glucose, insulin, HbA1c and HOMA-IR were observed compared to baseline.

Developmental Dyslexia: Insights from EEG-Based Findings and Molecular Signatures-A Pilot Study.

Developmental dyslexia (DD) is a learning disorder. Although risk genes have been identified, environmental factors, and particularly stress arising from constant difficulties, have been associated with the occurrence of DD by affecting brain plasticity and function, especially during critical neurodevelopmental stages. In this work, electroencephalogram (EEG) findings were coupled with the genetic and epigenetic molecular signatures of individuals with DD and matched controls. Specifically, we investigated the genetic and epigenetic correlates of key stress-associated genes (NR3C1, NR3C2, FKBP5, GILZ, SLC6A4) with psychological characteristics (depression, anxiety, and stress) often included in DD diagnostic criteria, as well as with brain EEG findings. We paired the observed brain rhythms with the expression levels of stress-related genes, investigated the epigenetic profile of the stress regulator glucocorticoid receptor (GR) and correlated such indices with demographic findings. This study presents a new interdisciplinary approach and findings that support the idea that stress, attributed to the demands of the school environment, may act as a contributing factor in the occurrence of the DD phenotype.

Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review.

Lung cancer is one of the deadliest cancers worldwide, with a high incidence rate, especially in tobacco smokers. Lung cancer accurate diagnosis is based on distinct histological patterns combined with molecular data for personalized treatment. Precise lung cancer classification from a single H&E slide can be challenging for a pathologist, requiring most of the time additional histochemical and special immunohistochemical stains for the final pathology report. According to WHO, small biopsy and cytology specimens are the available materials for about 70% of lung cancer patients with advanced-stage unresectable disease. Thus, the limited available diagnostic material necessitates its optimal management and processing for the completion of diagnosis and predictive testing according to the published guidelines. During the new era of Digital Pathology, Deep Learning offers the potential for lung cancer interpretation to assist pathologists' routine practice. Herein, we systematically review the current Artificial Intelligence-based approaches using histological and cytological images of lung cancer. Most of the published literature centered on the distinction between lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung carcinoma, reflecting the realistic pathologist's routine. Furthermore, several studies developed algorithms for lung adenocarcinoma predominant architectural pattern determination, prognosis prediction, mutational status characterization, and PD-L1 expression status estimation.

Variant-Related Differences in Laboratory Biomarkers among Patients Affected with Alpha, Delta and Omicron: A Retrospective Whole Viral Genome Sequencing and Hospital-Setting Cohort Study.

During the COVID-19 pandemic, different SARS-CoV-2 variants of concern (VOC) with specific characteristics have emerged and spread worldwide. At the same time, clinicians routinely evaluate the results of certain blood tests upon patient admission as well as during hospitalization to assess disease severity and the overall patient status. In the present study, we searched for significant cell blood count and biomarker differences among patients affected with the Alpha, Delta and Omicron VOCs at admission. Data from 330 patients were retrieved regarding age, gender, VOC, cell blood count results (WBC, Neut%, Lymph%, Ig%, PLT), common biomarkers (D-dimers, urea, creatinine, SGOT, SGPT, CRP, IL-6, suPAR), ICU admission and death. Statistical analyses were performed using ANOVA, the Kruskal-Wallis test, two-way ANOVA, Chi-square, T-test, the Mann-Whitney test and logistic regression was performed where appropriate using SPSS v.28 and STATA 14. Age and VOC were significantly associated with hospitalization, whereas significant differences among VOC groups were found for WBC, PLT, Neut%, IL-6, creatinine, CRP, D-dimers and suPAR. Our analyses showed that throughout the current pandemic, not only the SARS-CoV-2 VOCs but also the laboratory parameters that are used to evaluate the patient's status at admission are subject to changes.

Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review.

Colorectal cancer (CRC) is the second most common cancer in women and the third most common in men, with an increasing incidence. Pathology diagnosis complemented with prognostic and predictive biomarker information is the first step for personalized treatment. The increased diagnostic load in the pathology laboratory, combined with the reported intra- and inter-variability in the assessment of biomarkers, has prompted the quest for reliable machine-based methods to be incorporated into the routine practice. Recently, Artificial Intelligence (AI) has made significant progress in the medical field, showing potential for clinical applications. Herein, we aim to systematically review the current research on AI in CRC image analysis. In histopathology, algorithms based on Deep Learning (DL) have the potential to assist in diagnosis, predict clinically relevant molecular phenotypes and microsatellite instability, identify histological features related to prognosis and correlated to metastasis, and assess the specific components of the tumor microenvironment.

Alzheimer's Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods.

Dementia is the clinical syndrome characterized by progressive loss of cognitive and emotional abilities to a degree severe enough to interfere with daily functioning. Alzheimer's disease (AD) is the most common neurogenerative disorder, making up 50-70% of total dementia cases. Another dementia type is frontotemporal dementia (FTD), which is associated with circumscribed degeneration of the prefrontal and anterior temporal cortex and mainly affects personality and social skills. With the rapid advancement in electroencephalogram (EEG) sensors, the EEG has become a suitable, accurate, and highly sensitive biomarker for the identification of neuronal and cognitive dynamics in most cases of dementia, such as AD and FTD, through EEG signal analysis and processing techniques. In this study, six supervised machine-learning techniques were compared on categorizing processed EEG signals of AD and FTD cases, to provide an insight for future methods on early dementia diagnosis. K-fold cross validation and leave-one-patient-out cross validation were also compared as validation methods to evaluate their performance for this classification problem. The proposed methodology accuracy scores were 78.5% for AD detection with decision trees and 86.3% for FTD detection with random forests.

Machine Learning Algorithms and Statistical Approaches for Alzheimer's Disease Analysis Based on Resting-State EEG Recordings: A Systematic Review.

Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.

Endothelin-B receptors and ventricular arrhythmogenesis in the rat model of acute myocardial infarction.

The arrhythmogenic effects of endothelin-1 (ET-1) are mediated via ETA-receptors, but the role of ETB-receptors is unclear. We examined the pathophysiologic role of ETB-receptors on ventricular tachyarrhythmias (VT/VF) during myocardial infarction (MI). MI was induced by coronary ligation in two animal groups, namely in wild-type (n = 63) and in ETB-receptor-deficient (n = 61) rats. Using a telemetry recorder, VT/VF episodes were evaluated during phase I (the 1st hour) and phase II (2-24 h) post-MI, with and without prior beta-blockade. Action potential duration at 90% repolarization (APD90) was measured from monophasic epicardial recordings and indices of sympathetic activation were assessed using fast-Fourier analysis of heart rate variability. Serum epinephrine and norepinephrine were measured with radioimmunoassay. MI size was similar in the two groups. There was a marked temporal variation in VT/VF duration; during phase I, it was higher (p = 0.0087) in ETB-deficient (1,519 +/- 421 s) than in wild-type (190 +/- 34 s) rats, but tended (p = 0.086) to be lower in ETB-deficient (4.2 +/- 2.0 s) than in wild-type (27.7 +/- 8.0 s) rats during phase II. Overall, the severity of VT/VF was greater in ETB-deficient rats, evidenced by higher (p = 0.0058) mortality (72.0% vs. 32.1%). There was a temporal variation in heart rate and in the ratio of low- to high-frequency spectra, being higher (<0.001) during phase I, but lower (p < 0.05) during phase II in ETB-deficient rats. Likewise, 1 h post-MI, serum epinephrine (p = 0.025) and norepinephrine (p < 0.0001) were higher in ETB-deficient (4.20 +/- 0.54, 14.24 +/- 1.39 ng/ml) than in wild-type (2.30 +/- 0.59, 5.26 +/- 0.67 ng/ml) rats, respectively. After beta-blockade, VT/VF episodes and mortality were similar in the two groups. The ETB-receptor decreases sympathetic activation and arrhythmogenesis during the early phase of MI, but these effects diminish during evolving MI.

EEG Window Length Evaluation for the Detection of Alzheimer's Disease over Different Brain Regions.

Alzheimer's Disease (AD) is a neurogenerative disorder and the most common type of dementia with a rapidly increasing world prevalence. In this paper, the ability of several statistical and spectral features to detect AD from electroencephalographic (EEG) recordings is evaluated. For this purpose, clinical EEG recordings from 14 patients with AD (8 with mild AD and 6 with moderate AD) and 10 healthy, age-matched individuals are analyzed. The EEG signals are initially segmented in nonoverlapping epochs of different lengths ranging from 5 s to 12 s. Then, a group of statistical and spectral features calculated for each EEG rhythm (δ, θ, α, ß, and γ) are extracted, forming the feature vector that trained and tested a Random Forests classifier. Six classification problems are addressed, including the discrimination from whole-brain dynamics and separately from specific brain regions in order to highlight any alterations of the cortical regions. The results indicated a high accuracy ranging from 88.79% to 96.78% for whole-brain classification. Also, the classification accuracy was higher at the posterior and central regions than at the frontal area and the right side of temporal lobe for all classification problems.