A new EEG synchronization strength analysis method: S-estimator based normalized weighted-permutation mutual information

Cui, Dong; Pu, Weiting; Liu, Jing; Bian, Zhijie; Li, Qiuli; Wang, Lei; Gu, Guanghua

doi:10.1016/j.neunet.2016.06.004

Cited by 18 publications

(9 citation statements)

References 44 publications

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“…For the multichannel analysis, two approaches were adopted; in the first, the different temporal, spectral, and nonlinear parameters computed from the single channels were fed to the classifier. In the second, to estimate the synchronization degree of the different areas of the uterus, a bivariate method based on normalized permutation cross mutual information (NPCMI) [54], which has been proven to better discriminate imminent term labor [55], was computed from the different pairs of EHG channels. We then computed the mean efficiency index (MEI) of the different 3 Journal of Sensors parameters proposed in a previous work to define a more robust indicator of uterine electrical activity efficiency from multichannel recordings [55].…”

Section: Methodsmentioning

confidence: 99%

Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records

et al. 2019

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Preterm labor is one of the major causes of neonatal deaths and also the cause of significant health and development impairments in those who survive. However, there are still no reliable and accurate tools for preterm labor prediction in clinical settings. Electrohysterography (EHG) has been proven to provide relevant information on the labor time horizon. Many studies focused on predicting preterm labor by using temporal, spectral, and nonlinear parameters extracted from single EHG recordings. However, multichannel analysis, which includes information from the whole uterus and about coupling between the recording areas, may provide better results. The cross validation method is often used to design classifiers and evaluate their performance. However, when the validation dataset is used to tune the classifier hyperparameters, the performance metrics of this dataset may not properly assess its generalization capacity. In this work, we developed and compared different classifiers, based on artificial neural networks, for predicting preterm labor using EHG features from single and multichannel recordings. A set of temporal, spectral, nonlinear, and synchronization parameters computed from EHG recordings was used as the input features. All the classifiers were evaluated on independent test datasets, which were never “seen” by the models, to determine their generalization capacity. Classifiers’ performance was also evaluated when obstetrical data were included. The experimental results show that the classifier performance metrics were significantly lower in the test dataset (AUC range 76-91%) than in the train and validation sets (AUC range 90-99%). The multichannel classifiers outperformed the single-channel classifiers, especially when information was combined into mean efficiency indexes and included coupling information between channels. Including obstetrical data slightly improved the classifier metrics and reached an AUC of 91.1±2.5% for the test dataset. These results show promise for the transfer of the EHG technique to preterm labor prediction in clinical practice.

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Section: Methodsmentioning

confidence: 99%

Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records

et al. 2019

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“…All experiments are carried out within the two emotion dimensions of arousal and valence. (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45) bands. In addition, the frequency-domain features also include the difference of average PSD (4 power differences × 14 channel pairs) in theta, alpha, beta, and gamma bands for 14 EEG channel pairs (Fp2-Fp1, AF4-AF3, F4-F3, F8-F7, FC6-FC5, FC2-FC1, C4-C3, T8-T7, CP6-CP5, CP2-CP1, P4-P3, P8-P7, PO4-PO3, and O2-O1) between the right and left scalps.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, these high-level features are input to linear support vector machines to perform emotion recognition tasks. Synchronization analyses are extensively investigated in the neuroscience community, and synchronization measurements of EEG can characterize the underlying brain dynamics effectively [37][38][39]. Synchronization patterns of EEG signals change with changing emotion states.…”

Section: Introductionmentioning

confidence: 99%

Improved Deep Feature Learning by Synchronization Measurements for Multi-Channel EEG Emotion Recognition

et al. 2020

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Emotion recognition based on multichannel electroencephalogram (EEG) signals is a key research area in the field of affective computing. Traditional methods extract EEG features from each channel based on extensive domain knowledge and ignore the spatial characteristics and global synchronization information across all channels. This paper proposes a global feature extraction method that encapsulates the multichannel EEG signals into gray images. The maximal information coefficient (MIC) for all channels was first measured. Subsequently, an MIC matrix was constructed according to the electrode arrangement rules and represented by an MIC gray image. Finally, a deep learning model designed with two principal component analysis convolutional layers and a nonlinear transformation operation extracted the spatial characteristics and global interchannel synchronization features from the constructed feature images, which were then input to support vector machines to perform the emotion recognition tasks. Experiments were conducted on the benchmark dataset for emotion analysis using EEG, physiological, and video signals. The experimental results demonstrated that the global synchronization features and spatial characteristics are beneficial for recognizing emotions and the proposed deep learning model effectively mines and utilizes the two salient features.

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“…It is therefore unsurprising that understanding brain synchronization patterns has long been a central goal of neuroscience (Kandel et al, 2013 ), with respect to conditions such as epilepsy. The many applications of synchronization patterns in multivariate EEG include feature extraction (Mirowski et al, 2009 ), complex oscillator networks, neural computing (Cui et al, 2016 ), and brain disorder detection (Chen et al, 2014 ). Synchronization measurement of EEG represents an effective means of characterizing the underlying brain dynamics, e.g., identification and prediction of brain states.…”

Section: Introductionmentioning

confidence: 99%

“…Early studies on EEG synchronization focused on bivariate synchronous analysis, using measures such as the Pearson correlation coefficient, Spearman rank correlation, and mutual information (MI). MI is one of the most important information independence metrics (Cui et al, 2016 ), and it performs better than others in terms of anti-noise capability (Bonita et al, 2014 ). A difficult and unresolved problem in MI calculation is the determination of thresholds based on partitions.…”

Section: Introductionmentioning

confidence: 99%

Global Epileptic Seizure Identification With Affinity Propagation Clustering Partition Mutual Information Using Cross-Layer Fully Connected Neural Network

Wang

2018

Front. Hum. Neurosci.

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A longstanding challenge in epilepsy research and practice is the need to classify synchronization patterns hidden in multivariate electroencephalography (EEG) data that is routinely superimposed with intensive noise. It is essential to select a suitable feature extraction method to achieve high recognition performance. A typical approach is to extract the mutual information (MI) between pairs of channels. This calculation, which considers the differences between the sequence pairs to build a reasonable partition, can improve the classification performance. On this basis, however, it is even more difficult to adaptively classify the synchronization patterns hidden in multivariate EEG data under circumstances of insufficient a priori knowledge of domain dependency, such as denoising, feature extraction on a special patient, etc. To address these problems by (1) effectively calculating the MI matrix (synchronization pattern) and (2) accurately classifying the seizure or non-seizure state, this study first accurately measures the synchronization between channel pairs in terms of affinity propagation clustering partition MI (APCPMI). The global synchronization measurement is then obtained by organizing APCPMIs of all channel pairs into a correlation matrix. Finally, a cross-layer fully connected net is designed to characterize the synchronization dynamics correlation matrices adaptively and identify seizure or non-seizure states automatically. Experiments are performed using the CHB-MIT scalp EEG dataset to evaluate the proposed approach. Seizure states are identified with an accuracy, sensitivity, and specificity of 0.9793 ± 0.002, 0.9942 ± 0.0005, and 0.9676 ± 0.003, respectively; the resulting performance is superior to those achieved by most existing methods over the same dataset. Furthermore, the approach alleviates the necessity for strictly preprocessing (denoising, removing interferences and artifacts) the EEG data using prior knowledge, which is usually required by existing approaches.

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A new EEG synchronization strength analysis method: S-estimator based normalized weighted-permutation mutual information

Cited by 18 publications

References 44 publications

Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records

Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records

Improved Deep Feature Learning by Synchronization Measurements for Multi-Channel EEG Emotion Recognition

Global Epileptic Seizure Identification With Affinity Propagation Clustering Partition Mutual Information Using Cross-Layer Fully Connected Neural Network

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