Semi-supervised active learning using convolutional auto- encoder and contrastive learning.

Active learning is a field of machine learning that seeks to find the most efficient labels to annotate with a given budget, particularly in cases where obtaining labeled data is expensive or infeasible. This is becoming increasingly important with the growing success of learning-based methods, which often require large amounts of labeled data. Computer vision is one area where active learning has shown promise in tasks such as image classification, semantic segmentation, and object detection. In this research, we propose a pool-based semi-supervised active learning method for image classification that takes advantage of both labeled and unlabeled data. Many active learning approaches do not utilize unlabeled data, but we believe that incorporating these data can improve performance. To address this issue, our method involves several steps. First, we cluster the latent space of a pre-trained convolutional autoencoder. Then, we use a proposed clustering contrastive loss to strengthen the latent space's clustering while using a small amount of labeled data. Finally, we query the samples with the highest uncertainty to annotate with an oracle. We repeat this process until the end of the given budget. Our method is effective when the number of annotated samples is small, and we have validated its effectiveness through experiments on benchmark datasets. Our empirical results demonstrate the power of our method for image classification tasks in accuracy terms.

Using Brainwave Patterns Recorded from Plant Pathology Experts to Increase the Reliability of AI-Based Plant Disease Recognition System.

One of the most challenging problems associated with the development of accurate and reliable application of computer vision and artificial intelligence in agriculture is that, not only are massive amounts of training data usually required, but also, in most cases, the images have to be properly labeled before models can be trained. Such a labeling process tends to be time consuming, tiresome, and expensive, often making the creation of large labeled datasets impractical. This problem is largely associated with the many steps involved in the labeling process, requiring the human expert rater to perform different cognitive and motor tasks in order to correctly label each image, thus diverting brain resources that should be focused on pattern recognition itself. One possible way to tackle this challenge is by exploring the phenomena in which highly trained experts can almost reflexively recognize and accurately classify objects of interest in a fraction of a second. As techniques for recording and decoding brain activity have evolved, it has become possible to directly tap into this ability and to accurately assess the expert's level of confidence and attention during the process. As a result, the labeling time can be reduced dramatically while effectively incorporating the expert's knowledge into artificial intelligence models. This study investigates how the use of electroencephalograms from plant pathology experts can improve the accuracy and robustness of image-based artificial intelligence models dedicated to plant disease recognition. Experiments have demonstrated the viability of the approach, with accuracies improving from 96% with the baseline model to 99% using brain generated labels and active learning approach.

Identification of an early-stage Parkinson's disease neuromarker using event-related potentials, brain network analytics and machine-learning.

OBJECTIVE: The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson's disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms. BACKGROUND: Currently, diagnosis of PD depends mainly on motor signs and symptoms. However, there is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies. Cognitive impairment may appear before motor symptoms, and it tends to worsen with disease progression. While ERPs obtained during cognitive tasks performance represent processing stages of cognitive brain functions, they have not yet been established as sensitive or specific markers for early-stage PD. METHODS: Nineteen PD patients (disease duration of ≤2 years) and 30 healthy controls (HC) underwent EEG recording while performing visual Go/No-Go and auditory Oddball cognitive tasks. ERPs were analyzed by the BNA technology, and a ML algorithm identified a combination of features that distinguish early PD from HC. We used a logistic regression classifier with a 10-fold cross-validation. RESULTS: The ML algorithm identified a neuromarker comprising 15 BNA features that discriminated early PD patients from HC. The area-under-the-curve of the receiver-operating characteristic curve was 0.79. Sensitivity and specificity were 0.74 and 0.73, respectively. The five most important features could be classified into three cognitive functions: early sensory processing (P50 amplitude, N100 latency), filtering of information (P200 amplitude and topographic similarity), and response-locked activity (P-200 topographic similarity preceding the motor response in the visual Go/No-Go task). CONCLUSIONS: This pilot study found that BNA can identify patients with early PD using an advanced analysis of ERPs. These results need to be validated in a larger PD patient sample and assessed for people with premotor phase of PD.

Deep Brain Stimulation Can Differentiate Subregions of the Human Subthalamic Nucleus Area by EEG Biomarkers.

Introduction: Precise lead localization is crucial for an optimal clinical outcome of subthalamic nucleus (STN) deep brain stimulation (DBS) treatment in patients with Parkinson's disease (PD). Currently, anatomical measures, as well as invasive intraoperative electrophysiological recordings, are used to locate DBS electrodes. The objective of this study was to find an alternative electrophysiology tool for STN DBS lead localization. Methods: Sixty-one postoperative electrophysiology recording sessions were obtained from 17 DBS-treated patients with PD. An intraoperative physiological method automatically detected STN borders and subregions. Postoperative EEG cortical activity was measured, while STN low frequency stimulation (LFS) was applied to different areas inside and outside the STN. Machine learning models were used to differentiate stimulation locations, based on EEG analysis of engineered features. Results: A machine learning algorithm identified the top 25 evoked response potentials (ERPs), engineered features that can differentiate inside and outside STN stimulation locations as well as within STN stimulation locations. Evoked responses in the medial and ipsilateral fronto-central areas were found to be most significant for predicting the location of STN stimulation. Two-class linear support vector machine (SVM) predicted the inside (dorso-lateral region, DLR, and ventro-medial region, VMR) vs. outside [zona incerta, ZI, STN stimulation classification with an accuracy of 0.98 and 0.82 for ZI vs. VMR and ZI vs. DLR, respectively, and an accuracy of 0.77 for the within STN (DLR vs. VMR)]. Multiclass linear SVM predicted all areas with an accuracy of 0.82 for the outside and within STN stimulation locations (ZI vs. DLR vs. VMR). Conclusions: Electroencephalogram biomarkers can use low-frequency STN stimulation to localize STN DBS electrodes to ZI, DLR, and VMR STN subregions. These models can be used for both intraoperative electrode localization and postoperative stimulation programming sessions, and have a potential to improve STN DBS clinical outcomes.

Brain Network Analysis of EEG Recordings Can Be Used to Assess Cognitive Function in Teenagers With 15q13.3 Microdeletion Syndrome.

15q13.3 microdeletion syndrome causes a spectrum of cognitive disorders, including intellectual disability and autism. We assessed the ability of the EEG analysis algorithm Brain Network Analysis (BNA) to measure cognitive function in 15q13.3 deletion patients, and to differentiate between patient and control groups. EEG data was collected from 10 individuals with 15q13.3 microdeletion syndrome (14-18 years of age), as well as 30 age-matched healthy controls, as the subjects responded to Auditory Oddball (AOB) and Go/NoGo cognitive tasks. It was determined that BNA can be used to evaluate cognitive function in 15q13.3 microdeletion patients. This analysis also significantly differentiates between patient and control groups using 5 scores, all of which are produced from ERP peaks related to late cortical components that represent higher cognitive functions of attention allocation and response inhibition (P < 0.05).

Improved Back-Projection Cortical Potential Imaging by Multi-resolution Optimization Technique.

Electroencephalogram (EEG) has evolved to be a well-established tool for imaging brain activity. This progress is mainly due to the development of high-resolution (HR) EEG methods. One class of HR-EEG is the cortical potential imaging (CPI), which aims to estimate the potential distribution on the cortical surface, which is much more informative than EEG. Even though these methods exhibit good performance, most of them have inherent inaccuracies that originate from their operating principles that constrain the solution or require a complex calculation process. The back-projection CPI (BP-CPI) method is relatively new and has the advantage of being constraint-free and computation inexpensive. The method has shown relatively good accuracy, which is necessary to become a clinical tool. However, better performance must be achieved. In the present study, two improvements are proposed. Both are embedded as adjacent stages to the BP-CPI and are based on the multi-resolution optimization approach (MR-CPI). A series of Monte-Carlo simulations were performed to examine the characteristics of the proposed improvements. Additional tests were done, including different EEG noise levels and variation in electrode-numbers. The results showed highly accurate cortical potential estimations, with a reduction in estimation error by a factor of 3.75 relative to the simple BP-CPI estimation error. We also validated these results with true EEG data. Analyzing these EEGs, we have demonstrated the MR-CPI competence to correctly localize cortical activations in a real environment. The MR-CPI methods were shown to be reliable for estimating cortical potentials, enabling researchers to obtain fast and robust high-resolution EEGs.

Biological and Behavioral Patterns of Post-Stroke Depression in Rats.

BACKGROUND: Post-stroke depression (PSD) is the most frequent psychiatric complication following ischemic stroke. It affects up to 60% of all patients and is associated with increased morbidity and mortality following ischemic stroke. The pathophysiology of PSD remains elusive and appears to be multifactorial, rather than "purely" biological or psychosocial in origin. Thus, valid animal models of PSD would contribute to the study of the etiology (and treatment) of this disorder. METHODS: The present study depicts a rat model for PSD, using middle cerebral artery occlusion (MCAO). The two-way shuttle avoidance task, Porsolt forced-swim test, and sucrose preference test were employed to assess any depression-like behavior. Localized brain expressions of brain-derived neurotrophic factor (BDNF) protein levels were evaluated to examine the possible involvement of the brain neuronal plasticity in the observed behavioral syndrome. The raw data were subjected to unsupervised fuzzy clustering (UFC) algorithms to assess the sensitivity of bio-behavioral measures indicative of depressive symptoms post MCAO. RESULTS: About 56% of the rats developed significant depressive-like behavioral disruptions as a result of MCAO compared with 4% in the sham-operated control rats. A pattern of a depressive-like behavioral response was common to all affected MCAO animals, characterized by significantly more escape failures and reduced number of total avoidance shuttles, a significant elevation in immobility duration, and reduced sucrose preference. Significant downregulations of BDNF protein levels in the hippocampal sub-regions, frontal cortex, and hypothalamus were observed in all affected MCAO animals. CONCLUSION: The UFC analysis supports the behavioral analysis and thus, lends validity to our results.

Noninvasive blood potassium measurement using signal-processed, single-lead ecg acquired from a handheld smartphone.

OBJECTIVE: We have previously used a 12-lead, signal-processed ECG to calculate blood potassium levels. We now assess the feasibility of doing so with a smartphone-enabled single lead, to permit remote monitoring. PATIENTS AND METHODS: Twenty-one hemodialysis patients held a smartphone equipped with inexpensive FDA-approved electrodes for three 2min intervals during hemodialysis. Individualized potassium estimation models were generated for each patient. ECG-calculated potassium values were compared to blood potassium results at subsequent visits to evaluate the accuracy of the potassium estimation models. RESULTS: The mean absolute error between the estimated potassium and blood potassium 0.38±0.32 mEq/L (9% of average potassium level) decreasing to 0.6 mEq/L using predictors of poor signal. CONCLUSIONS: A single-lead ECG acquired using electrodes attached to a smartphone device can be processed to calculate the serum potassium with an error of 9% in patients undergoing hemodialysis. SUMMARY: A single-lead ECG acquired using electrodes attached to a smartphone can be processed to calculate the serum potassium in patients undergoing hemodialysis remotely.

Back-Projection Cortical Potential Imaging: Theory and Results.

Electroencephalography (EEG) is the single brain monitoring technique that is non-invasive, portable, passive, exhibits high-temporal resolution, and gives a directmeasurement of the scalp electrical potential. Amajor disadvantage of the EEG is its low-spatial resolution, which is the result of the low-conductive skull that "smears" the currents coming from within the brain. Recording brain activity with both high temporal and spatial resolution is crucial for the localization of confined brain activations and the study of brainmechanismfunctionality, whichis then followed by diagnosis of brain-related diseases. In this paper, a new cortical potential imaging (CPI) method is presented. The new method gives an estimation of the electrical activity on the cortex surface and thus removes the "smearing effect" caused by the skull. The scalp potentials are back-projected CPI (BP-CPI) onto the cortex surface by building a well-posed problem to the Laplace equation that is solved by means of the finite elements method on a realistic head model. A unique solution to the CPI problem is obtained by introducing a cortical normal current estimation technique. The technique is based on the same mechanism used in the well-known surface Laplacian calculation, followed by a scalp-cortex back-projection routine. The BP-CPI passed four stages of validation, including validation on spherical and realistic head models, probabilistic analysis (Monte Carlo simulation), and noise sensitivity tests. In addition, the BP-CPI was compared with the minimum norm estimate CPI approach and found superior for multi-source cortical potential distributions with very good estimation results (CC >0.97) on a realistic head model in the regions of interest, for two representative cases. The BP-CPI can be easily incorporated in different monitoring tools and help researchers by maintaining an accurate estimation for the cortical potential of ongoing or event-related potentials in order to have better neurological inferences from the EEG.

Distinctive cardiac autonomic dysfunction following stress exposure in both sexes in an animal model of PTSD.

It is unclear whether the poor autonomic flexibility or dysregulation observed in patients with posttraumatic stress disorder (PTSD) represents a pre-trauma vulnerability factor or results from exposure to trauma. We used an animal model of PTSD to assess the association between the behavioral response to predator scent stress (PSS) and the cardiac autonomic modulation in male and female rats. The rats were surgically implanted with radiotelemetry devices to measure their electrocardiograms and locomotor activity (LMA). Following baseline telemetric monitoring, the animals were exposed to PSS or sham-PSS. Continuous telemetric monitoring (24h/day sampling) was performed over the course of 7days. The electrocardiographic recordings were analyzed using the time- and frequency-domain indexes of heart rate variability (HRV). The behavioral response patterns were assessed using the elevated plus maze and acoustic startle response paradigms for the retrospective classification of individuals according to the PTSD-related cut-off behavioral criteria. During resting conditions, the male rats had significantly higher heart rates (HR) and lower HRV parameters than the female rats during both the active and inactive phases of the daily cycle. Immediately after PSS exposure, both the female and male rats demonstrated a robust increase in HR and a marked drop in HRV parameters, with a shift of sympathovagal balance towards sympathetic predominance. In both sexes, autonomic system habituation and recovery were selectively inhibited in the rats whose behavior was extremely disrupted after exposure to PSS. However, in the female rats, exposure to the PSS produced fewer EBR rats, with a more rapid recovery curve than that of the male rats. PSS did not induce changes to the circadian rhythm of the LMA. According to our results, PTSD can be conceptualized as a disorder that is related to failure-of-recovery mechanisms that impede the restitution of physiological homeostasis.

Novel Bloodless Potassium Determination Using a Signal-Processed Single-Lead ECG.

BACKGROUND: Hyper- and hypokalemia are clinically silent, common in patients with renal or cardiac disease, and are life threatening. A noninvasive, unobtrusive, blood-free method for tracking potassium would be an important clinical advance. METHODS AND RESULTS: Two groups of hemodialysis patients (development group, n=26; validation group, n=19) underwent high-resolution digital ECG recordings and had 2 to 3 blood tests during dialysis. Using advanced signal processing, we developed a personalized regression model for each patient to noninvasively calculate potassium values during the second and third dialysis sessions using only the processed single-channel ECG. In addition, by analyzing the entire development group's first-visit data, we created a global model for all patients that was validated against subsequent sessions in the development group and in a separate validation group. This global model sought to predict potassium, based on the T wave characteristics, with no blood tests required. For the personalized model, we successfully calculated potassium values with an absolute error of 0.36±0.34 mmol/L (or 10% of the measured blood potassium). For the global model, potassium prediction was also accurate, with an absolute error of 0.44±0.47 mmol/L for the training group (or 11% of the measured blood potassium) and 0.5±0.42 for the validation set (or 12% of the measured blood potassium). CONCLUSIONS: The signal-processed ECG derived from a single lead can be used to calculate potassium values with clinically meaningful resolution using a strategy that requires no blood tests. This enables a cost-effective, noninvasive, unobtrusive strategy for potassium assessment that can be used during remote monitoring.

Multimodal Neural Network for Rapid Serial Visual Presentation Brain Computer Interface.

Brain computer interfaces allow users to preform various tasks using only the electrical activity of the brain. BCI applications often present the user a set of stimuli and record the corresponding electrical response. The BCI algorithm will then have to decode the acquired brain response and perform the desired task. In rapid serial visual presentation (RSVP) tasks, the subject is presented with a continuous stream of images containing rare target images among standard images, while the algorithm has to detect brain activity associated with target images. In this work, we suggest a multimodal neural network for RSVP tasks. The network operates on the brain response and on the initiating stimulus simultaneously, providing more information for the BCI application. We present two variants of the multimodal network, a supervised model, for the case when the targets are known in advanced, and a semi-supervised model for when the targets are unknown. We test the neural networks with a RSVP experiment on satellite imagery carried out with two subjects. The multimodal networks achieve a significant performance improvement in classification metrics. We visualize what the networks has learned and discuss the advantages of using neural network models for BCI applications.

Brain Network Activation Analysis Utilizing Spatiotemporal Features for Event Related Potentials Classification.

The purpose of this study was to introduce an improved tool for automated classification of event-related potentials (ERPs) using spatiotemporally parcellated events incorporated into a functional brain network activation (BNA) analysis. The auditory oddball ERP paradigm was selected to demonstrate and evaluate the improved tool. Methods: The ERPs of each subject were decomposed into major dynamic spatiotemporal events. Then, a set of spatiotemporal events representing the group was generated by aligning and clustering the spatiotemporal events of all individual subjects. The temporal relationship between the common group events generated a network, which is the spatiotemporal reference BNA model. Scores were derived by comparing each subject's spatiotemporal events to the reference BNA model and were then entered into a support vector machine classifier to classify subjects into relevant subgroups. The reliability of the BNA scores (test-retest repeatability using intraclass correlation) and their utility as a classification tool were examined in the context of Target-Novel classification. Results: BNA intraclass correlation values of repeatability ranged between 0.51 and 0.82 for the known ERP components N100, P200, and P300. Classification accuracy was high when the trained data were validated on the same subjects for different visits (AUCs 0.93 and 0.95). The classification accuracy remained high for a test group recorded at a different clinical center with a different recording system (AUCs 0.81, 0.85 for 2 visits). Conclusion: The improved spatiotemporal BNA analysis demonstrates high classification accuracy. The BNA analysis method holds promise as a tool for diagnosis, follow-up and drug development associated with different neurological conditions.

Preliminary evidence of reduced brain network activation in patients with post-traumatic migraine following concussion.

Post-traumatic migraine (PTM) (i.e., headache, nausea, light and/or noise sensitivity) is an emerging risk factor for prolonged recovery following concussion. Concussions and migraine share similar pathophysiology characterized by specific ionic imbalances in the brain. Given these similarities, patients with PTM following concussion may exhibit distinct electrophysiological patterns, although researchers have yet to examine the electrophysiological brain activation in patients with PTM following concussion. A novel approach that may help differentiate brain activation in patients with and without PTM is brain network activation (BNA) analysis. BNA involves an algorithmic analysis applied to multichannel EEG-ERP data that provides a network map of cortical activity and quantitative data during specific tasks. A prospective, repeated measures design was used to evaluate BNA (during Go/NoGo task), EEG-ERP, cognitive performance, and concussion related symptoms at 1, 2, 3, and 4 weeks post-injury intervals among athletes with a medically diagnosed concussion with PTM (n = 15) and without (NO-PTM) (n = 22); and age, sex, and concussion history matched controls without concussion (CONTROL) (n = 20). Participants with PTM had significantly reduced BNA compared to NO-PTM and CONTROLS for Go and NoGo components at 3 weeks and for NoGo component at 4 weeks post-injury. The PTM group also demonstrated a more prominent deviation of network activity compared to the other two groups over a longer period of time. The composite BNA algorithm may be a more sensitive measure of electrophysiological change in the brain that can augment established cognitive assessment tools for detecting impairment in individuals with PTM.

Convolutional Neural Network for Multi-Category Rapid Serial Visual Presentation BCI.

Brain computer interfaces rely on machine learning (ML) algorithms to decode the brain's electrical activity into decisions. For example, in rapid serial visual presentation (RSVP) tasks, the subject is presented with a continuous stream of images containing rare target images among standard images, while the algorithm has to detect brain activity associated with target images. Here, we continue our previous work, presenting a deep neural network model for the use of single trial EEG classification in RSVP tasks. Deep neural networks have shown state of the art performance in computer vision and speech recognition and thus have great promise for other learning tasks, like classification of EEG samples. In our model, we introduce a novel spatio-temporal regularization for EEG data to reduce overfitting. We show improved classification performance compared to our earlier work on a five categories RSVP experiment. In addition, we compare performance on data from different sessions and validate the model on a public benchmark data set of a P300 speller task. Finally, we discuss the advantages of using neural network models compared to manually designing feature extraction algorithms.

"Clinical brain profiling": a neuroscientific diagnostic approach for mental disorders.

Clinical brain profiling is an attempt to map a descriptive nosology in psychiatry to underlying constructs in neurobiology and brain dynamics. This paper briefly reviews the motivation behind clinical brain profiling (CBP) and presents some provisional validation using clinical assessments and meta-analyses of neuroscientific publications. The paper has four sections. In the first, we review the nature and motivation for clinical brain profiling. This involves a description of the key aspects of functional anatomy that can lead to psychopathology. These features constitute the dimensions or categories for a profile of brain disorders based upon pathophysiology. The second section describes a mapping or translation matrix that maps from symptoms and signs, of a descriptive sort, to the CBP dimensions that provide a more mechanistic explanation. We will describe how this mapping engenders archetypal diagnoses, referring readers to tables and figures. The third section addresses the construct validity of clinical brain profiling by establishing correlations between profiles based on clinical ratings of symptoms and signs under classical diagnostic categories with the corresponding profiles generated automatically using archetypal diagnoses. We then provide further validation by performing a cluster analysis on the symptoms and signs and showing how they correspond to the equivalent brain profiles based upon clinical and automatic diagnosis. In the fourth section, we address the construct validity of clinical brain profiling by looking for associations between pathophysiological mechanisms (such as connectivity and plasticity) and nosological diagnoses (such as schizophrenia and depression). Based upon the mechanistic perspective offered in the first section, we test some particular hypotheses about double dissociations using a meta-analysis of PubMed searches. The final section concludes with perspectives for the future and outstanding validation issues for clinical brain profiling.

Brain Network Activation (BNA) reveals scopolamine-induced impairment of visual working memory.

The overarching goal of this event-related potential (ERP) study was to examine the effects of scopolamine on the dynamics of brain network activation using a novel ERP network analysis method known as Brain Network Activation (BNA). BNA was used for extracting group-common stimulus-activated network patterns elicited to matching probe stimuli in the context of a delayed matching-to-sample task following placebo and scopolamine treatments administered to healthy participants. The BNA extracted networks revealed the existence of two pathophysiological mechanisms following scopolamine, disconnection, and compensation. Specifically, weaker frontal theta and parietal alpha coupling was accompanied with enhanced fronto-centro-parietal theta activation relative to placebo. In addition, using the characteristic BNA network of each treatment as well as corresponding literature-guided selective subnetworks as combined biomarkers managed to differentiate between individual responses to each of the treatments. Behavioral effects associated with scopolamine included delayed response time and impaired response accuracy. These results indicate that the BNA method is sensitive to the effects of scopolamine on working memory and that it may potentially enable diagnosis and treatment assessment of dysfunctions associated with cholinergic deficiency.

Spatiotemporal representations of rapid visual target detection: a single-trial EEG classification algorithm.

Brain computer interface applications, developed for both healthy and clinical populations, critically depend on decoding brain activity in single trials. The goal of the present study was to detect distinctive spatiotemporal brain patterns within a set of event related responses. We introduce a novel classification algorithm, the spatially weighted FLD-PCA (SWFP), which is based on a two-step linear classification of event-related responses, using fisher linear discriminant (FLD) classifier and principal component analysis (PCA) for dimensionality reduction. As a benchmark algorithm, we consider the hierarchical discriminant component Analysis (HDCA), introduced by Parra, et al. 2007. We also consider a modified version of the HDCA, namely the hierarchical discriminant principal component analysis algorithm (HDPCA). We compare single-trial classification accuracies of all the three algorithms, each applied to detect target images within a rapid serial visual presentation (RSVP, 10 Hz) of images from five different object categories, based on single-trial brain responses. We find a systematic superiority of our classification algorithm in the tested paradigm. Additionally, HDPCA significantly increases classification accuracies compared to the HDCA. Finally, we show that presenting several repetitions of the same image exemplars improve accuracy, and thus may be important in cases where high accuracy is crucial.

Passive fetal monitoring by adaptive wavelet denoising method.

Fetal Heart Rate (FHR) monitoring is one of the most important fetal well being tests. Existing FHR monitoring methods are based on Doppler ultrasound technique, which has several disadvantages. Passive fetal monitoring by phonocardiography is an appropriate alternative; however, its implementation is a challenging task due to low energy of fetal heart sounds and multiple interference signals presence. In this paper, an advanced signal processing method for passive fetal monitoring based on adaptive wavelet denoising is presented. The method's performance is compared with Doppler ultrasound monitor. The results show 94-97.5% accuracy, including highly disturbed cases.

Early post-stressor intervention with propranolol is ineffective in preventing posttraumatic stress responses in an animal model for PTSD.

The therapeutic value of ß-adrenoceptor blockage, using propranolol, in the aftermath of traumatic experience is uncertain. A prospective, controlled animal model of posttraumatic stress disorder (PTSD) was employed to assess the effects of propranolol on long-term behavioral responses to stress. Animals exposed to predator scent stress received a single bolus of propranolol (10 or 15mg/kg) or vehicle 1h post-exposure. Outcomes were assessed using the elevated plus-maze (EPM) and acoustic startle response (ASR) at 30days and freezing response to a trauma reminder (unsoiled litter) on Day 31. Individual animals were classified as having "extreme", "partial" and "minimal" behavioral responses, according to pre-set cut-off criteria for EPM and ASR response patterns. The physiological efficacy of the doses of propranolol was verified by collecting cardiovascular data telemetrically (from exposed or unexposed individuals given propranolol or vehicle). The effect of propranolol on long-term memory was verified using a non-spatial memory task. Both doses of propranolol effectively reduced mean heart rate and impaired the object-recognition task, as expected. No significant effect on prevalence rates of PTSD-like behavioral responses or on trauma reminder response was observed for either dose of propranolol as compared to vehicle. Despite adequate efficacy in terms of heart rate and disruption of memory, single-dose, post-stress ß-blockage with propranolol was ineffective in reducing onset of PTSD-like behavioral disruption and trauma cue responses in the long term. Traumatic stress-related processes appear to be affected differently than the others.