Multiresolution directed transfer function approach for segment-wise seizure classification of epileptic EEG signal

Arterial Blood Pressure (ABP) and photoplethysmography (PPG) are both useful techniques to monitor cardiovascular status. Though ABP monitoring is more widely employed, this procedure of signal acquisition whether done invasively or non-invasively may cause inconvenience and discomfort to the patients. PPG, however, is simple, noninvasive, and can be used for continuous measurement. This paper focuses on analyzing the similarities in time and frequency domains between ABP and PPG signals for normotensive, prehypertensive and hypertensive subjects and the feasibility of the classification of subjects considering the results of the analysis performed. From a database with 120 records of ABP and PPG, each 120 s in length, the records where separated into epochs taking into account 10 heartbeats, and the following statistical measures were performed: Correlation (r), Coherence (COH), Partial Coherence (pCOH), Partial Directed Coherence (PDC), Directed Transfer Function (DTF), Full Frequency Directed Transfer Function (ffDTF) and Direct Directed Transfer Function (dDTF). The correlation coefficient was r > 0.9 on average for all groups, indicating a strong morphology similarity. For COH and pCOH, coherence (linear correlation in frequency domain) was found with significance (p < 0.01) in differentiating between normotensive and hypertensive subjects using PPG signals. For the dataset at hand, only two synchrony measures are able to convincingly distinguish hypertensive subjects from normotensive control subjects, i.e., ffDTF and dDTF. From PDC, DTF, ffDTF, and dDTF, a consistent, a strong significant causality from ABP→PPG was found. When all synchrony measures were combined, an 87.5 % accuracy was achieved to detect hypertension using a Neural Network classifier, suggesting that PPG holds most informative features that exist in ABP.

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Mental Arithmetic Task Recognition Using Effective Connectivity and Hierarchical Feature Selection from EEG Signals

Basic and Clinical Neuroscience Journal ◽

10.32598/bcn.2021.2034.1 ◽

2021 ◽

Author(s):

Ahmad Shalbaf ◽

◽

Arash Maghsoudi ◽

Keyword(s):

Feature Selection ◽

Transfer Function ◽

Mental Arithmetic ◽

Brain Connectivity ◽

Effective Connectivity ◽

Concave Minimization ◽

Autism Spectrum ◽

Feature Subset Selection ◽

Eeg Signal ◽

Directed Transfer Function

Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recognition systems rely on features of a single channel of EEG; however, the relationships among EEG channels in the form of effective brain connectivity analysis can contain valuable information. The aim of this paper is to identify a set of discriminative effective brain connectivity features from EEG signal and develop a hierarchical feature selection structure for classification of mental arithmetic and baseline tasks effectively. Methods: We estimated effective connectivity using Directed Transfer Function (DTF), direct Directed Transfer Function (dDTF) and Generalized Partial Directed Coherence (GPDC) methods. These measures determine the causal relation between different brain areas. To select most significant effective connectivity features, a hierarchical feature subset selection method is used. First Kruskal–Wallis test was performed and consequently, five feature selection algorithms namely Support Vector Machine ( SVM ) method based on Recursive Feature Elimination, Fisher score, mutual information, minimum Redundancy Maximum Relevance and concave minimization and SVM are used to select the best discriminative features. Finally, SVM method was used for classification. Results: Results show that the best EEG classification performance in 29 participants and 60 trials is obtained using GPDC and feature selection via concave minimization method in Beta2 (15−22Hz) frequency band with 89% accuracy. Conclusion: This new hierarchical automated system could be useful for discrimination of mental arithmetic and baseline tasks from EEG signal effectively.

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