Humcintosh6002

The transition from wake to sleep is a continuum that is well characterized by the electroencephalogram (EEG) power spectral ratio (ρ) between the beta (15 to 30 Hz) and theta (4 to 8 Hz) bands. From selleck compound to sleep, the value of ρ gradually decreases.We have designed and implemented a single EEG-signal based closed-loop system that leverages ρ to modulate the volume of a pink-noise type of audio such that the volume becomes gradually softer as sleep initiates. A proof-of-concept trial was conducted with this system and it was found that using this concept resulted in a reduction of sleep latency and latency to deep sleep.Quantification of brain-heart interplay (BHI) has mainly been performed in the time and frequency domains. However, such functional interactions are likely to involve nonlinear dynamics associated with the two systems. To this extent, in this preliminary study we investigate the functional coupling between multifractal properties of Electroencephalography (EEG) and Heart Rate Variability (HRV) series using a channel- and time scale-wise maximal information coefficient analysis. Experimental results were gathered from 24 healthy volunteers undergoing a resting state and a cold-pressure test, and suggest that significant changes between the two experimental conditions might be associated with nonlinear quantifiers of the multifractal spectrum. Particularly, major brain-heart functional coupling was associated with the secondorder cumulant of the multifractal spectrum. We conclude that a functional nonlinear relationship between brain- and heartbeat-related multifractal sprectra exist, with higher values associated with the resting state.We propose a novel computational framework for the estimation of functional directional brain-to-heart interplay in an instantaneous fashion. The framework is based on inhomogeneous point-process models for human heartbeat dynamics and employs inverse-Gaussian probability density functions characterizing the timing of R-peak events. The instantaneous estimation of the functional directional coupling is based on the definition of point-process transfer entropy, which is here retrieved from heart rate variability (HRV) and Electroencephalography (EEG) power spectral series gathered from 12 healthy subjects undergoing significant sympathovagal changes induced by a cold-pressor test. Results suggest that EEG oscillations dynamically influence heartbeat dynamics with specific time delays in the 30-60s and 90-120s ranges, and through a functional activity over specific cortical regions.The growing interest in the study of functional brain-heart interplay (BHI) has motivated the development of novel methodological frameworks for its quantification. While a combination of electroencephalography (EEG) and heartbeat-derived series has been widely used, the role of EEG preprocessing on a BHI quantification is yet unknown. To this extent, here we investigate on four different EEG electrical referencing techniques associated with BHI quantifications over 4-minute resting-state in 15 healthy subjects. BHI methods include the synthetic data generation model, heartbeat-evoked potentials, heartbeat-evoked oscillations, and maximal information coefficient (MIC). EEG signals were offline referenced under the Cz channel, common average, mastoids average, and Laplacian method, and statistical comparisons were performed to assess similarities between references and between BHI techniques. #link# Results show a topographical agreement between BHI estimation methods depending on the specific EEG reference. Major differences between BHI methods occur with the Laplacian reference, while major differences between EEG references are with the MIC analysis. We conclude that the choice of EEG electrical reference may significantly affect a functional BHI quantification.Quantification of directed (nonlinear) brain-heart interactions has turned to be an emerging topic of research and is important for the better understanding of central autonomic processing during specific diseases such as schizophrenia. Convergent Cross Mapping (CCM) was able to provide directed, frequency-selective and topographic views on existent interaction pattern of those patients. Investigations of the influence of individual heart rate (HR) on CCM estimations may further contribute to this topic. Relationship of mean HR and CCM was analyzed in a group of schizophrenic patients (N=17) and healthy controls (N=21). Influence of individual HR values was most pronounced for patients, for interactions from brain to heart and for the subgroup of patients with highest mean HR values.The use of feature extraction and selection from EEG signals has shown to be useful in the detection of epileptic seizure segments. However, these traditional methods have more recently been surpassed by deep learning techniques, forgoing the need for complex feature engineering. This work aims to extend the conventional approach of epileptic seizure detection utilizing raw power spectra of EEG signals and convolutional neural networks (CNN). The proposed technique utilizes wavelet transform to compute the frequency characteristics of multi-channel EEG signals. The EEG signals are divided into 2 second epochs and frequency spectrum up to a cutoff frequency of 45 Hz is computed. This multi-channel raw spectral data forms the input to a one-dimensional CNN (1-D CNN). Spectral data from the current, previous, and next epochs is utilized for predicting the label of the current epoch. The performance of the technique is evaluated using a dataset of EEG signals from 24 cases. The proposed method achieves an accuracy of 97.25% in detecting epileptic seizure segments. This result shows that multi-channel EEG wavelet power spectra and 1-D CNN are useful in detecting epileptic seizures.Epileptic seizure prediction explores the probability of forecasting the onset of epileptic seizure, which aids to timely treatment for patients. It provides a time lead compared to traditional seizure detection. In this paper, a spectral feature extraction is developed and the seizure prediction is performed based on uncorrelated multilinear discriminant analysis (UMLDA) and Support Vector Machine (SVM). To make best use of information in different dimension, we construct a three-order tensor in temporal, spectral and spatial domain by wavelet transform. And UMLDA implements the tensor-to-vector projection (TVP) with the minimum redundancy. The proposed solution employed 23 subjects' Electroencephalogram (EEG) data from Boston Children's Hospital-MIT scalp EEG dataset, each subject contains 40 minutes EEG signal. For the classification task of ictal state and preictal state, it exhibits an overall accuracy of 95%.