A broadband method of quantifying phase synchronization for discriminating seizure EEG signals

Investigation of phase synchronization in functional brain networks of children with ADHD using nonlinear recurrence measure

Measuring the phase synchronization between different brain regions in functional brain networks is a common approach to investigate many psychological disorders such as Attention Deficit Hyperactivity Disorder (ADHD). The emotional processing deficit in ADHD children is one of the main obstacles in their social interactions. In this study, the nonlinear Correlation between Probability of Recurrences (CPR) method is used for the first time to construct functional brain networks of 22 boys with ADHD and 22 healthy ones during watching four visual-emotional stimuli types. Topological features of brain networks, including shortest path length, clustering coefficient, and nodes strengths, are investigated in groups of ADHD and healthy. The results indicate a significantly (P-Values < 0.01) greater average clustering coefficient and lower shortest path length in the brain networks of ADHD individuals than the healthy ones. Accordingly, in the ADHD brain networks, the information exchange in both local and global scales is abnormally more than the healthy ones, leading to a hyper-synchronization in this group. The topological alterations of ADHD brain networks are mainly observed in the brain's frontal and occipital lobes, indicating impaired brain function of this group in emotional and visual processing. This survey demonstrates that the CPR method can be a good candidate for distinguishing the phase interactions of ADHD and healthy brain networks. Therefore, this study can contribute to further insights into the nonlinear dynamics analysis of brain networks in ADHD individuals.

Epileptic Seizure Detection by Cascading Isolation Forest-based Anomaly Screening and EasyEnsemble

The electroencephalogram (EEG), for measuring the electrophysiological activity of the brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG-based seizure detection algorithms have already yielded high sensitivity, but training those algorithms requires a large amount of labelled data. Data labelling is often done with a lot of human efforts, which is very time-consuming. In this study, we propose a hybrid system integrating an unsupervised learning (UL) module and a supervised learning (SL) module, where the UL module can significantly reduce the workload of data labelling. For preliminary seizure screening, UL synthesizes amplitude-integrated EEG (aEEG) extraction, isolation forest-based anomaly detection, adaptive segmentation, and silhouette coefficient-based anomaly detection evaluation. The UL module serves to quickly locate the determinate subjects (seizure segments and seizure-free segments) and the indeterminate subjects (potential seizure candidates). Afterwards, more robust seizure detection for the indeterminate subjects is performed by the SL using an EasyEnsemble algorithm. EasyEnsemble, as a class-imbalance learning method, can potentially decrease the generalization error of the seizure-free segments. The proposed method can significantly reduce the workload of data labelling while guaranteeing satisfactory performance. The proposed seizure detection system is evaluated using the Children's Hospital Boston - Massachusetts Institute of Technology (CHB-MIT) scalp EEG dataset, and it achieves a mean accuracy of 92.62%, a mean sensitivity of 95.55%, and a mean specificity of 92.57%. To the best of our knowledge, this is the first epilepsy seizure detection study employing the integration of both the UL and the SL modules, achieving a competitive performance superior or similar to that of the state-of-the-art methods.

Detection of Mild Cognitive Impairment with MEG Functional Connectivity Using Wavelet-Based Neuromarkers

Studies on developing effective neuromarkers based on magnetoencephalographic (MEG) signals have been drawing increasing attention in the neuroscience community. This study explores the idea of using source-based magnitude-squared spectral coherence as a spatial indicator for effective regions of interest (ROIs) localization, subsequently discriminating the participants with mild cognitive impairment (MCI) from a group of age-matched healthy control (HC) elderly participants. We found that the cortical regions could be divided into two distinctive groups based on their coherence indices. Compared to HC, some ROIs showed increased connectivity (hyper-connected ROIs) for MCI participants, whereas the remaining ROIs demonstrated reduced connectivity (hypo-connected ROIs). Based on these findings, a series of wavelet-based source-level neuromarkers for MCI detection are proposed and explored, with respect to the two distinctive ROI groups. It was found that the neuromarkers extracted from the hyper-connected ROIs performed significantly better for MCI detection than those from the hypo-connected ROIs. The neuromarkers were classified using support vector machine (SVM) and k-NN classifiers and evaluated through Monte Carlo cross-validation. An average recognition rate of 93.83% was obtained using source-reconstructed signals from the hyper-connected ROI group. To better conform to clinical practice settings, a leave-one-out cross-validation (LOOCV) approach was also employed to ensure that the data for testing was from a participant that the classifier has never seen. Using LOOCV, we found the best average classification accuracy was reduced to 83.80% using the same set of neuromarkers obtained from the ROI group with functional hyper-connections. This performance surpassed the results reported using wavelet-based features by approximately 15%. Overall, our work suggests that (1) certain ROIs are particularly effective for MCI detection, especially when multi-resolution wavelet biomarkers are employed for such diagnosis; (2) there exists a significant performance difference in system evaluation between research-based experimental design and clinically accepted evaluation standards.

Nonlinear Phase Synchronization Analysis of EEG Signals in Amnesic Mild Cognitive Impairment with Type 2 Diabetes Mellitus

Studying the nonlinear synchronization of electroencephalogram (EEG) in type 2 diabetic mellitus (T2DM) to find the EEG characteristics related to cognitive impairment is beneficial to the early prevention and diagnosis of mild cognitive impairment. Correlation between probabilities of recurrence (CPR) is a nonlinear phase synchronization method based on recurrence and recurrence probability, which had shown its superiority in detecting epilepsy. In this study, CPR method was used for the first time to analyze the synchronization of eye-closed resting EEG signals with T2DM. The 27 participants were divided into amnesic mild cognitive impairment (aMCI) group (17 case) and control group (10 cases with age and education matched). The CPR values in two groups were statistically analyzed by Mann-Whitney U test, and the correlation between EEG synchronization and cognitive function was studied by Spearman's correlation. The results showed that aMCI group had lower CPR values at each electrode pair than control group, and two groups had decreased CPR values with the increase of the spatial distance of the electrode pair in inter hemispheric. The CPR values were significantly different in frontal, parietal and temporal regions in intra hemispheric between two groups. The CPR values of C3-F7, F4-C4 and FP2-T6 were significantly positively correlated with the MOCA values. This study showed that the synchronization values of EEG signals obtained by the CPR method were significantly different between aMCI and control group, and they were the EEG characteristics associated with cognitive impairment in T2DM.

Estimating multiple latencies in the auditory system from auditory steady-state responses on a single EEG channel

The latency of the auditory steady-state response (ASSR) may provide valuable information regarding the integrity of the auditory system, as it could potentially reveal the presence of multiple intracerebral sources. To estimate multiple latencies from high-order ASSRs, we propose a novel two-stage procedure that consists of a nonparametric estimation method, called apparent latency from phase coherence (ALPC), followed by a heuristic sequential forward selection algorithm (SFS). Compared with existing methods, ALPC-SFS requires few prior assumptions, and is straightforward to implement for higher-order nonlinear responses to multi-cosine sound complexes with their initial phases set to zero. It systematically evaluates the nonlinear components of the ASSRs by estimating multiple latencies, automatically identifies involved ASSR components, and reports a latency consistency index. To verify the proposed method, we performed simulations for several scenarios: two nonlinear subsystems with different or overlapping outputs. We compared the results from our method with predictions from existing, parametric methods. We also recorded the EEG from ten normal-hearing adults by bilaterally presenting superimposed tones with four frequencies that evoke a unique set of ASSRs. From these ASSRs, two major latencies were found to be stable across subjects on repeated measurement days. The two latencies are dominated by low-frequency (LF) (near 40 Hz, at around 41-52 ms) and high-frequency (HF) (> 80 Hz, at around 21-27 ms) ASSR components. The frontal-central brain region showed longer latencies on LF components, but shorter latencies on HF components, when compared with temporal-lobe regions. In conclusion, the proposed nonparametric ALPC-SFS method, applied to zero-phase, multi-cosine sound complexes is more suitable for evaluating embedded nonlinear systems underlying ASSRs than existing methods. It may therefore be a promising objective measure for hearing performance and auditory cortex (dys)function.