Epileptic spikes detector in pediatric EEG based on matched filters and neural networks

Comparative Study of the Effect of Oral Chloral Hydrate and Intranasal Fentanyl on Sedation in Children for Electroencephalography

Objectives

Sedation and stability during electroencephalography (EEG) in pediatrics have high clinical importance. This study compares the sedative properties of oral chloral hydrate (OCH) and intranasal fentanyl (INF).

Materials & Methods

This study was a randomized clinical trial conducted in 2020 in Isfahan City on sixty-two pediatric candidates for EEG. Patients were randomized into two groups receiving 50 mg/kg OCH and 2 μg/kg INF thirty minutes before the process. The heart rate (HR), mean arterial pressure (MAP), respiratory rate (RR), and oxygen saturation (O2 sat) of patients, sedation, and physician's satisfaction were measured and compared between groups.

Results

The HR of patients decreased significantly in both groups (P< 0.001), and the patients that received INF had significantly lower HR 15, 30, 45, and 60 minutes after drug administrations (P< 0.05). RR evaluation indicated significantly decreased RR in both groups (P< 0.001), and patients receiving INF had lower RR 30, 45, and 60 per minutes after drug administrations (P< 0.001). Both groups showed significantly increased sedation levels during the study (P< 0.001), and patients treated with INF had higher sedation levels 15, 30, and 45 minutes after drug administration. Satisfaction rates were higher among the group that received INF (P= 0.020).

Conclusion

The use of INF had significant analgesic and sedative effects on pediatrics undergoing EEG.

Abnormal phase-amplitude coupling characterizes the interictal state in epilepsy

OBJECTIVE

 Diagnosing epilepsy still requires visual interpretation of electroencephalography and magnetoencephalography (MEG) by specialists, which prevents quantification and standardization of diagnosis. Previous studies proposed automated diagnosis by combining various features from electroencephalography and MEG, such as relative power (Power) and functional connectivity. However, the usefulness of interictal phase-amplitude coupling (PAC) in diagnosing epilepsy is still unknown. We hypothesized that resting-state PAC would be different for patients with epilepsy in the interictal state and for healthy participants such that it would improve discrimination between the groups.

METHODS

 We obtained resting-state MEG and magnetic resonance imaging in 90 patients with epilepsy during their preoperative evaluation and in 90 healthy participants. We used the cortical currents estimated from MEG and magnetic resonance imaging to calculate Power in the δ (1-3 Hz), θ (4-7 Hz), α (8-13 Hz), β (13-30 Hz), low γ (35-55 Hz), and high γ (65-90 Hz) bands and functional connectivity in the θ band. PAC was evaluated using the synchronization index (SI) for eight frequency band pairs: the phases of δ, θ, α, and β and the amplitudes of low and high γ. First, we compared the mean SI values for the patients with epilepsy and the healthy participants. Then, using features such as PAC, Power, functional connectivity, and features extracted by deep learning individually or combined, we tested whether PAC improves discrimination accuracy for the two groups.

RESULTS

 The mean SI values were significantly different for the patients with epilepsy and the healthy participants. The SI value difference was highest for θ/low γ in the temporal lobe. Discrimination accuracy was the highest, at 90%, using the combination of PAC and deep learning.

SIGNIFICANCE

 Abnormal PAC characterized the patients with epilepsy in the interictal state compared with the healthy participants, potentially improving the discrimination of epilepsy.

An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals

Identifying relevant data to support the automatic analysis of electroencephalograms (EEG) has become a challenge. Although there are many proposals to support the diagnosis of neurological pathologies, the current challenge is to improve the reliability of the tools to classify or detect abnormalities. In this study, we used an ensemble feature selection approach to integrate the advantages of several feature selection algorithms to improve the identification of the characteristics with high power of differentiation in the classification of normal and abnormal EEG signals. Discrimination was evaluated using several classifiers, i.e., decision tree, logistic regression, random forest, and Support Vecctor Machine (SVM); furthermore, performance was assessed by accuracy, specificity, and sensitivity metrics. The evaluation results showed that Ensemble Feature Selection (EFS) is a helpful tool to select relevant features from the EEGs. Thus, the stability calculated for the EFS method proposed was almost perfect in most of the cases evaluated. Moreover, the assessed classifiers evidenced that the models improved in performance when trained with the EFS approach’s features. In addition, the classifier of epileptiform events built using the features selected by the EFS method achieved an accuracy, sensitivity, and specificity of 97.64%, 96.78%, and 97.95%, respectively; finally, the stability of the EFS method evidenced a reliable subset of relevant features. Moreover, the accuracy, sensitivity, and specificity of the EEG detector are equal to or greater than the values reported in the literature.