Photoplethysmography and Heart Rate Variability for the Diagnosis of Preeclampsia.

BACKGROUND: The goal of this study was to determine a set of timing, shape, and statistical features available through noninvasive monitoring of maternal electrocardiogram and photoplethysmography that identifies preeclamptic patients. METHODS: Pregnant women admitted to Labor and Delivery were monitored with pulse oximetry and electrocardiogram for 30 minutes. Photoplethysmogram features and heart rate variability were extracted from each data set and applied to a sequential feature selection algorithm to discriminate women with preeclampsia with severe features, from normotensive and hypertensive controls. The classification boundary was chosen to minimize the expected misclassification cost. The prior probabilities of the misclassification costs were assumed to be equal. RESULTS: Thirty-seven patients with clinically diagnosed preeclampsia with severe features were compared with 43 normotensive controls; all were in early labor or beginning induction. Six variables were used in the final model. The area under the receiver operating characteristic curve was 0.907 (standard error [SE] = 0.004) (sensitivity 78.2% [SE = 0.3%], specificity 89.9% [SE = 0.1%]) with a positive predictive value of 0.883 (SE = 0.001). Twenty-eight subjects with chronic or gestational hypertension were compared with the same preeclampsia group, generating a model with 5 features with an area under the curve of 0.795 (SE = 0.007; sensitivity 79.0% [SE = 0.2%], specificity 68.7% [SE = 0.4%]), and a positive predictive value of 0.799 (SE = 0.002). CONCLUSIONS: Vascular parameters, as assessed noninvasively by photoplethysmography and heart rate variability, may have a role in screening women suspected of having preeclampsia, particularly in areas with limited resources.

A New Multi-Resolution Approach to EEG Brain Modeling Using Local-Global Graphs and Stochastic Petri-Nets.

Recent modeling of brain activities encompasses the fusion of different modalities. However, fusing brain modalities requires not only the efficient and compatible representation of the signals but also the benefits associated with it. For instance, the combination of the functional characteristics of EEGs with the structural features of functional magnetic resonance imaging contributes to a better interpretation localization of brain activities. In this paper, we consider the EEG signals as parallel 2D string images from which we extract their visual abstract representations of EEG features. This representation can benefit not only the EEG modeling of the signals but also a future fusion with another modality, like fMRI. In particular, the new methodology, called Bar-LG, provides a reduced discretization of the EEG signals into selected minima/maxima in order to be used in a form of tokens for EEG brain activities of interest. A formal context-free language is used to express and represent the extracted tokens for the selected active brain regions. Then, a Generalized Stochastic Petri-Nets (GSPN) model is used for expressing the functional associations and interactions of these EEG signals as 2D image regions. An illustrative EEG example of epileptic seizure is presented to show the Bar-LG methodology's abstract capabilities.

A decision support framework for the discrimination of children with controlled epilepsy based on EEG analysis.

BACKGROUND: In this work we consider hidden signs (biomarkers) in ongoing EEG activity expressing epileptic tendency, for otherwise normal brain operation. More specifically, this study considers children with controlled epilepsy where only a few seizures without complications were noted before starting medication and who showed no clinical or electrophysiological signs of brain dysfunction. We compare EEG recordings from controlled epileptic children with age-matched control children under two different operations, an eyes closed rest condition and a mathematical task. The aim of this study is to develop reliable techniques for the extraction of biomarkers from EEG that indicate the presence of minor neurophysiological signs in cases where no clinical or significant EEG abnormalities are observed. METHODS: We compare two different approaches for localizing activity differences and retrieving relevant information for classifying the two groups. The first approach focuses on power spectrum analysis whereas the second approach analyzes the functional coupling of cortical assemblies using linear synchronization techniques. RESULTS: Differences could be detected during the control (rest) task, but not on the more demanding mathematical task. The spectral markers provide better diagnostic ability than their synchronization counterparts, even though a combination (or fusion) of both is needed for efficient classification of subjects. CONCLUSIONS: Based on these differences, the study proposes concrete biomarkers that can be used in a decision support system for clinical validation. Fusion of selected biomarkers in the Theta and Alpha bands resulted in an increase of the classification score up to 80% during the rest condition. No significant discrimination was achieved during the performance of a mathematical subtraction task.

Functional Connectivity Analysis of Cerebellum Using Spatially Constrained Spectral Clustering.

The human cerebellum contains almost 50% of the neurons in the brain, although its volume does not exceed 10% of the total brain volume. The goal of this study is to derive the functional network of the cerebellum during the resting-state and then compare the ensuing group networks between males and females. Toward this direction, a spatially constrained version of the classic spectral clustering algorithm is proposed and then compared against conventional spectral graph theory approaches, such as spectral clustering, and N-cut, on synthetic data as well as on resting-state fMRI data obtained from the Human Connectome Project (HCP). The extracted atlas was combined with the anatomical atlas of the cerebellum resulting in a functional atlas with 46 regions of interest. As a final step, a gender-based network analysis of the cerebellum was performed using the data-driven atlas along with the concept of the minimum spanning trees. The simulation analysis results confirm the dominance of the spatially constrained spectral clustering approach in discriminating activation patterns under noisy conditions. The network analysis results reveal statistically significant differences in the optimal tree organization between males and females. In addition, the dominance of the left VI lobule in both genders supports the results reported in a previous study of ours. To our knowledge, the extracted atlas comprises the first resting-state atlas of the cerebellum based on HCP data.

The independent components of auditory P300 and CNV evoked potentials derived from single-trial recordings.

The back-projected independent components (BICs) of single-trial, auditory P300 and contingent negative variation (CNV) evoked potentials (EPs) were derived using independent component analysis (ICA) and cluster analysis. The method was tested in simulation including a study of the electric dipole equivalents of the signal sources. P300 data were obtained from healthy and Alzheimer's disease (AD) subjects. The BICs were of approximately 100 ms duration and approximated positive- and negative-going half-sinusoids. Some positively and negatively peaking BICs constituting the P300 coincided with known peaks in the averaged P300. However, there were trial-to-trial differences in their occurrences, particularly where a positive or a negative BIC could occur with the same latency in different trials, a fact which would be obscured by averaging them. These variations resulted in marked differences in the shapes of the reconstructed, artefact-free, single-trial P300s. The latencies of the BIC associated with the P3b peak differed between healthy and AD subjects (p < 0.01). More reliable evidence than that obtainable from single-trial or averaged P300s is likely to be found by studying the properties of the BICs over a number of trials. For the CNV, BICs corresponding to both the orienting and the expectancy components were found.

Classification of EEG Single Trial Microstates Using Local Global Graphs and Discrete Hidden Markov Models.

We present a novel synergistic methodology for the spatio-temporal analysis of single Electroencephalogram (EEG) trials. This new methodology is based on the novel synergy of Local Global Graph (LG graph) to characterize define the structural features of the EEG topography as a global descriptor for robust comparison of dominant topographies (microstates) and Hidden Markov Models (HMM) to model the topographic sequence in a unique way. In particular, the LG graph descriptor defines similarity and distance measures that can be successfully used for the difficult comparison of the extracted LG graphs in the presence of noise. In addition, hidden states represent periods of stationary distribution of topographies that constitute the equivalent of the microstates in the model. The transitions between the different microstates and the formed syntactic patterns can reveal differences in the processing of the input stimulus between different pathologies. We train the HMM model to learn the transitions between the different microstates and express the syntactic patterns that appear in the single trials in a compact and efficient way. We applied this methodology in single trials consisting of normal subjects and patients with Progressive Mild Cognitive Impairment (PMCI) to discriminate these two groups. The classification results show that this approach is capable to efficiently discriminate between control and Progressive MCI single trials. Results indicate that HMMs provide physiologically meaningful results that can be used in the syntactic analysis of Event Related Potentials.

Combining EEG Microstates with fMRI Structural Features for Modeling Brain Activity.

Combining information from Electroencephalography (EEG) and Functional Magnetic Resonance Imaging (fMRI) has been a topic of increased interest recently. The main advantage of the EEG is its high temporal resolution, in the scale of milliseconds, while the main advantage of fMRI is the detection of functional activity with good spatial resolution. The advantages of each modality seem to complement each other, providing better insight in the neuronal activity of the brain. The main goal of combining information from both modalities is to increase the spatial and the temporal localization of the underlying neuronal activity captured by each modality. This paper presents a novel technique based on the combination of these two modalities (EEG, fMRI) that allow a better representation and understanding of brain activities in time. EEG is modeled as a sequence of topographies, based on the notion of microstates. Hidden Markov Models (HMMs) were used to model the temporal evolution of the topography of the average Event Related Potential (ERP). For each model the Fisher score of the sequence is calculated by taking the gradient of the trained model parameters. The Fisher score describes how this sequence deviates from the learned HMM. Canonical Partial Least Squares (CPLS) were used to decompose the two datasets and fuse the EEG and fMRI features. In order to test the effectiveness of this method, the results of this methodology were compared with the results of CPLS using the average ERP signal of a single channel. The presented methodology was able to derive components that co-vary between EEG and fMRI and present significant differences between the two tasks.

Using Dynamic Bayesian Networks for modeling EEG topographic sequences.

In this work we present a methodology for modeling the trajectory of EEG topography over time, using Dynamic Bayesian Networks (DBNs). Based on the microstate model we are using DBNs to model the evolution of the EEG topography. Analysis of the microstate model is being usually limited in the wide band signal or an isolated band. We are using Coupled Hidden Markov Models (CHMM) and a two level influence model in order to model the temporal evolution and the coupling of the topography states in three bands, delta, theta and alpha. We are applying this methodology for the classification of target and non-target single trial from a visual detection task. The results indicate that taking under consideration the interaction among the different bands improves the classification of single trials.

Activity detection and causal interaction analysis among independent EEG components from memory related tasks.

Over the past few years there has been an increased interest in studying the underlying neural mechanism of cognitive brain activity related to memory. In this direction, we study the brain activity based on its independent components instead of the EEG signal itself aiming towards identifying and analyzing induced responses being attributed to oscillatory bursts from local or distant neural assemblies, with variable latency and frequency, in an auditory working memory paradigm. The contribution and functional coupling of independent components to evoked and/or induced oscillatory activities is investigated through the concept of the recently introduced partial directed coherence method, which can also reveal the direction of the statistically significant relationships. The results on read data from an oddball experiment are in accordance with previous psychophysiology studies suggesting increased phase locked activity most prominently in the delta/ theta band, while alpha is also apparent in measures of non phase-locked activity. Dynamic synchronization is inferred between the alpha and delta bands, whereas some influence of the theta band is also detected. This study indicates that functional connectivity during cognitive processes may be successfully assessed using spectral power measures applied on independent components, which reflect distinct spatial patterns of activity.

Assessment of neural dynamic coupling and causal interactions between independent EEG components from cognitive tasks using linear and nonlinear methods.

Over the past few years there has been an increased interest in studying the underlying neural mechanism of cognitive brain activity. In this direction, we study the brain activity based on its independent components instead of the EEG signal itself. Both linear and nonlinear synchronization measures are applied to EEG components, which are free of volume conduction effects and background noise. More specifically, a robust nonlinear state-space generalized synchronization assessment method and the recently introduced partial directed coherence are investigated in a working memory paradigm, during mental rehearsal of pictures. The latter is a linear method able to assess not only the independence of the brain regions, but also the direction of the statistically significant relationships. The results are in accordance with previous psychophysiology studies suggesting increased synchrony between prefrontal and parietal components during the rehearsal process, most prominently in gamma (ca. 40 Hz) band. This study indicates that functional connectivity during cognitive processes may be successfully assessed using independent components, which reflect distinct spatial patterns of activity.

The effects of breast augmentation surgery on future ability to lactate.

The majority of women receiving breast augmentation surgery do so at a time in their lives when both reproduction and lactation are common. It does not occur to most women to consider the possible effects breast augmentation surgery may have on their future ability to exclusively breast-feed their baby. Most women raise concerns about their inability to exclusively breast-feed years after surgery when they have a child. It is therefore important that women considering breast augmentation surgery be fully informed of the possible effects surgery may have on their future ability to lactate. The possible direct effects of surgery on the breast tissue and the complications of breast surgery on future ability to lactate are discussed. Surgical technique, i.e., implant type and placement, are also discussed. The types of incisions made into the breast tissue and the positioning of the implants once inside the breast parenchyma are analyzed and their possible effects on future ability to lactate are explored. Women who undergo breast augmentation surgery have a greater incidence of lactation insufficiency. Factors directly related to the surgical procedure as well as short- and long-term complications of surgery compromise future ability to exclusively breast-feed a baby. Factors directly related to surgery include severing of the lateral and medial branches of the fourth intercostal nerve or the nerve endings of the nipple-areolar complex, which, lead to reduced sensation and loss of the suckling reflex resulting in decreased milk production. Hematoma formation increases the risk of developing capsular contracture therefore necessitating the need for further surgical intervention. Infection also requires further intervention and as a result, further risk to the breast tissue. Long-term breast pain, capsular contracture, and pressure effects on the breast from the implant are all possible long-term complications that compromise a woman's future ability to lactate and exclusively breast-feed her baby. With good surgical technique and proper postoperative management, most of the complications associated with surgery that may result in insufficient milk production can be minimized but not always avoided. Compared with nonaugmented women, women who have had augmentation surgery have a higher incidence of lactation insufficiency.