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Unilateral vocal collapse paralysis post-thyroidectomy: does early input permit better speech restoration?
In particular, this study initially demonstrates how the variations within the 24 hours values of specific HRV feature-sets could effectively reveal prognostic information about the evolution of sepsis, prior to the actual clinical diagnosis. Moreover, this study demonstrates that differences in the values of a particular set of features at 22 hours before the actual clinical diagnosis/symptoms can be reliably used to train a convolutional neural network for automatic classification between the individuals in the sepsis and non-sepsis groups with 88.89±7.86% accuracy.Empathy which can understand and respond to the unique affective experiences of others plays an essential role in social interaction. Although many neuroimaging studies have investigated the neural mechanisms underlying empathy for social pain, how its mechanisms are modulated by trait empathy remains unknown. The present event-related potential (ERP) study used Chatroom Interact Task to examine how trait empathy modulates brain response to empathy for social rejection. The behavior results showed that participants were less pleasant when observing rejection compared to observing acceptance in both high- and low-levels empathy groups. The ERP results revealed more negative-going N2 for social acceptance compared to rejection in both groups, but there was no difference in N2 between high- and low- empathy group. However, the late components, i.e., the P3b, N400 and LPP, revealed significant difference between social acceptance and rejection in high empathic participants rather than low empathic participants. These findings suggested that individuals with high empathic traits could devote more attention and mental resources to process observing ostracism.Short-duration bursts of spontaneous activity are important markers of maturation in the electroencephalogram (EEG) of premature infants. This paper examines the application of a feature-less machine learning approach for detecting these bursts. EEGs were recorded over the first 3 days of life for infants with a gestational age below 30 weeks. Bursts were annotated on the EEG from 36 infants. In place of feature extraction, the time-series EEG is transformed into a time-frequency distribution (TFD). A gradient boosting machine is then trained directly on the whole TFD using a leave-one-out procedure. TFD kernel parameters, length of the Doppler and lag windows, are selected within a nested cross-validation procedure during training. Results indicate that detection performance is sensitive to Doppler-window length but not lag-window length. Median area under the receiver operator characteristic for detection is 0.881 (inter-quartile range 0.850 to 0.913). Examination of feature importance highlights a critical wideband region less then 15 Hz in the TFD. Burst detection methods form an important component in any fully-automated brain-health index for the vulnerable preterm infant.The N2pc event-related potential component measures direction and time course of selective visual attention and represents an important biomarker in cognitive neuroscience. While its subtractive origin strongly influences the amplitude, thus hindering its detection, other external factors, such as subject's inefficiency to allocate attention to the cued target, or the heterogeneity of the visual context, may strongly affect the elicitation of the component itself. It would therefore be extremely important to create a tool that, using as few sweeps as possible, could reliably establish whether an N2pc is present in an individual subject. In the present work, we propose an approach by resorting to a time-frequency analysis of N2pc individual signals; in particular, power at each frequency band (α/β/δ/θ) was computed in the N2 time range and correlated to the estimated amplitude of the N2pc. Preliminary results on fourteen human volunteers of a visual search design showed a very high correlation coefficient (over 0.9) between the low frequency bands power and the mean absolute amplitude of the component, using only 40 sweeps. Results also seemed to suggest that N2pc amplitude values higher than 0.5 μV could be accurately classified according to time-frequency indices.Clinical Relevance - The online detection of the N2pc presence in individual EEG datasets would allow not only to study the factors responsible of N2pc variability across subjects and conditions, but also to investigate novel search variants on participants with a predisposition to show an N2pc, reducing time and costs and the possibility to obtain biased results.Diagnosis of hypoxic-ischemic encephalopathy (HIE) is currently limited and prognostic biological markers are required for early identification of at risk infants at birth. Using pre-clinical data from our fetal sheep models, we have shown that micro-scale EEG patterns, such as high-frequency spikes and sharp waves, evolve superimposed on a significantly suppressed background during the early hours of recovery (0-6 h), after an HI insult. In particular, we have demonstrated that the number of micro-scale gamma spike transients peaks within the first 2-2.5 hours of the insult and automatically quantified sharp waves in this period are predictive of neural outcome. This period of time is optimal for the initiation of neuroprotection treatments such as therapeutic hypothermia, which has a limited window of opportunity for implementation of 6 h or less after an HI insult. Clinically, it is hard to determine when an insult has started and thus the window of opportunity for treatment. Thus, reliable automatic algorithms that could accurately identify EEG patterns that denote the phase of injury is a valuable clinical tool. We have previously developed successful machine-learning strategies for the identification of HI micro-scale EEG patterns in a preterm fetal sheep model of HI. This paper employs, for the first time, reverse biorthogonal Wavelet-Scalograms (WS) as the inputs to a 17-layer deep-trained convolutional neural network (CNN) for the precise identification of high-frequency micro-scale spike transients that occur in the 80-120Hz gamma band during first 2 h period of an HI insult. check details The rbio-WS-CNN classifier robustly identified spike transients with an exceptionally high-performance of 99.82%.Clinical relevance-The suggested classifier would effectively identify and quantify EEG patterns of a similar morphology in preterm newborns during recovery from an HI-insult.
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