Activity

Heart Rate Variability is a significant indicator of the Autonomic Neural System’s functioning, traditionally evaluated from electrocardiogram recordings. Photoplethysmography sensors, like electrocardiograph devices, track the heart’s activity and have been widely popularized by their use in smart watches and fitness trackers. In this study we develop a deep learning based approach which is able to successfully estimate the patient’s Root Mean Square of the Successive Differences, a common heart rate variability metric, from lower quality, less expensive photoplethysmography sensors under a wide range of conditions.There have been many previous studies on brain electrical activity and attention function, but the research on observing the cognitive function of attention from frequency brain electrical indicators remains insufficient. This study proposed an attentional network test (ANT) of Chinese version and used frequency analysis methods to observe the power spectrum activity and functional connectivity of delta (δ), theta (θ), alpha (α) bands of EEG signals to further understand their relationship with attention networks. The attentional network test was composed of alerting network, orienting network and execute conflict network, and these networks were compared with the resting state in different frequency bands. The results showed that α band activity was significantly suppressed in all three attentional states, and the power of θ band activity dramatically increased for the execute conflict network. The negative connection of α band in the long distance (frontal lobe to parietal lobe or occipital lobe) might be a sign of resting state network, and the positive connections between δ and θ band in similar areas could be an indicator of execute conflict network. This pilot study suggests that the frequency domain analysis of EEG signals could be a great tool to visualize the brain activities in response to different attentional networks.Clinical Relevance- This pilot study proved that the frequency bands activity might be suitable objective neuro-markers to distinguish different attention states.Epilepsy is frequently characterized by convulsive seizures, which are often followed by a postictal EEG suppression state (PGES). The ability to automatically detect and monitor seizure progression and postictal state can allow for early warning of seizure onset, timely intervention in seizures themselves, as well as identification of major complications in epilepsy such as status epilepticus and sudden unexpected death in epilepsy (SUDEP). To test whether it is possible to reliably differentiate these ictal and postictal states, we investigated 52 seizure records (both intracranial and scalp EEG) from 19 patients. Phase-amplitude cross-frequency coupling was calculated for each recording and used as an input to a convolutional neural network model, achieving the mean accuracy of 0.890.09 across all classes, with the worst class accuracy of 0.73 for one of the later ictal sub-states. When the trained model was applied to SUDEP patient data, it classified seizure recordings as primarily interictal and PGES-like state (70% and 26%, respectively), highlighting the fact that in SUDEP patients seizures primarily exist in postictal states and don’t show the ictal sub-state evolution. These results suggest that using frequency coupling markers with a machine learning algorithm can reliably identify ictal and postictal sub-states, which can open up opportunities for novel monitoring and management approaches in epilepsy.Sleep staging is of paramount importance in children with suspicion of pediatric obstructive sleep apnea (OSA). Complexity, cost, and intrusiveness of overnight polysomnography (PSG), the gold standard, have led to the search for alternative tests. In this sense, the photoplethysmography signal (PPG) carries useful information about the autonomous nervous activity associated to sleep stages and can be easily acquired in pediatric sleep apnea home tests with a pulse oximeter. selleck chemicals In this study, we use the PPG signal along with convolutional neural networks (CNN), a deep-learning technique, for the automatic identification of the three main levels of sleep wake (W), rapid eye movement (REM), and non-REM sleep. A database of 366 PPG recordings from pediatric OSA patients is involved in the study. A CNN architecture was trained using 30-s epochs from the PPG signal for three-stage sleep classification. This model showed a promising diagnostic performance in an independent test set, with 78.2% accuracy and 0.57 Cohen’s kappa for W/NREM/REM classification. Furthermore, the percentage of time in wake stage obtained for each subject showed no statistically significant differences with the manually scored from PSG. These results were superior to the only state-of-the-art study focused on the analysis of the PPG signal in the automated detection of sleep stages in children suffering from OSA. This suggests that CNN can be used along with PPG recordings for sleep stages scoring in pediatric home sleep apnea tests.Electroencephalogram (EEG) signals have shown to be a good source of information for emotion recognition algorithms in Human-Brain interaction applications. In this paper, a reproducible framework is proposed for classifying human emotions based on EEG signals. The framework consists of extracting frequency-dependent features from raw EEG signals to form a three-dimensional EEG image which is classified by a convolutional neural network (CNN). The framework is used to show that the 3D input CNN outperforms conventional methods with two-dimensional input, using a public dataset. The implementation of the framework is publicly available to facilitate further work on this topic https//github.com/KvanNoord/3D-CNN-EEG-Emotion-Classification.Combining electroencephalography (EEG) to functional near-infrared spectroscopy (fNIRS) is a promising technique that has gained momentum thanks to their complementarity. While EEG measures the electrical activity of the brain, fNIRS records the variations in cerebral blood flow and related hemoglobin concentrations. However, both modalities are typically contaminated with artefacts. Muscle and eye artefacts, affect the EEG signals, while hemodynamic and oxygenation changes in the extracerebral compartment due to systemic changes (superficial layer) corrupt the fNIRS signals. Moreover, both signals are sensitive to sensor motion artefacts characterized by large amplitude. There are several well-established methods for removing artefacts for both modalities. The objective of this paper is to apply a common approach to denoise both EEG and fNIRS signals. Indeed Artifact Subspace Reconstruction (ASR) method, which is an automatic, online-capable and efficient method for deleting transient or large-amplitude EEG artefacts, can be a good alternative to also denoise fNIRS signals. In this paper, we first propose, a new more comprehensive formulation of ASR. Then, we study the effectiveness of the method in denoising both the EEG and fNIRS signals.Capturing the error perception of a human interacting with a Brain-Computer Interface (BCI) is a key piece in improving the accuracy of these systems and making the interaction more seamless. Convolutional Neural Networks (CNN) have recently been applied for this task rendering the model free of feature-selection. We propose a new model with shorter temporal input trying to approximate its usability to that of a real-time BCI application. We evaluate and compare our model with some other recent CNN models using the Monitoring Error-Related Potential dataset, obtaining an accuracy of 80% with a sensitivity and specificity of 76% and 85%, respectively. These results outperform previous models. All models are made available online for reproduction and peer review.The recent development of novel multi-electrode recording technologies has revealed the existence of traveling patterns of cortical activity in many species and under different states of awareness. Among these, slow activation waves occurring under sleep and anesthesia have been widely investigated as they provide unique insights into network features such as excitability, connectivity, structure, and dynamics of the cerebral cortex. Such characterization is usually based on clustering methods which are constrained by a priori assumptions as to the number of clusters to be used or rely on wave-by-wave pattern reconstruction. Here, we introduce a new computational tool based on modal analysis of fluid flows which is robustly applied to multivariate electrophysiological data from cortical networks, namely the Energy-based Hierarchical Waves Clustering method (EHWC). EHWC is composed of three main steps (1) detecting the occurrence of global waves; (2) reducing the data dimensionality via singular value decomposition; (3) clustering hierarchically the singled-out waves. The analysis does not require the single-channel contribution to the waves, which is a typical bottleneck in this kind of analysis due to the unavoidable intrinsic variability of locally recorded activity. For testing and validation, here we used in vivo extracellular recordings from mice cortex under three different levels of anesthesia. As a result, we found slow waves with an increasing number of propagation modes as the anesthesia level decreases, giving an estimate of the increasing complexity of network dynamics. This and other wave’s features replicate and extend the findings from previous literature, paving the way to extend the same approach to non-invasive electrophysiological recordings like EEG and fMRI used clinically for the characterization of brain dynamics and clinical stratification in brain lesions.Studies on intracranial electroencephalography (icEEG) recordings of patients with drug resistant epilepsy (DRE) show that epilepsy biomarkers propagate in time across brain areas. Here, we propose a novel method that estimates critical features of these propagations for different epilepsy biomarkers (spikes, ripples, and fast ripples), and assess their common onset as a reliable biomarker of the epileptogenic zone (EZ). For each biomarker, an automatic algorithm ranked the icEEG electrodes according to their timing occurrence in propagations and finally dichotomized them as onset or spread. We validated our algorithm on icEEG recordings of 8 children with DRE having a good surgical outcome (Engel score = 1). We estimated the overlap of the onset, spread, and entire zone of propagation with the resection (RZ) and the seizure onset zone (SOZ). Spike and ripple propagations were seen in all patients, whereas fast ripple propagations were seen in 6 patients. Spike, ripple, and fast ripple propagations had a mean duration of 28.3 ± 24.3 ms, 38.7 ± 37 ms, and 25 ± 14 ms respectively. Onset electrodes predicted the RZ and SOZ with higher specificity compared to the entire zone for all three biomarkers (p less then 0.05). Overlap of spike and ripple onsets presented a higher specificity than each separate biomarker onset for the SOZ, the onsets overlap was more specific (97.89 ± 2.36) than the ripple onset (p=0.031); for the RZ, the onsets overlap was more specific (98.48 ± 1.5) than the spike onset (p=0.016). Yet, the entire zone for spike and ripple propagations predicted the RZ with higher sensitivity compared to each biomarker’s onset (or spread) (p less then 0.05). We present, for the first time, preliminary evidence from icEEG data that fast ripples propagate in time across large areas of the brain. The onsets overlap of spike and ripple propagations constitutes an extremely specific (but not sensitive) biomarker of the EZ, compared to areas of spread (and entire areas) in propagation.