Automated Detection of Seizure Types from the Higher-Order Moments of Maximal Overlap Wavelet Distribution.

In this work, an attempt has been made to develop an automated system for detecting electroclinical seizures such as tonic-clonic seizures, complex partial seizures, and electrographic seizures (EGSZ) using higher-order moments of scalp electroencephalography (EEG). The scalp EEGs of the publicly available Temple University database are utilized in this study. The higher-order moments, namely skewness and kurtosis, are extracted from the temporal, spectral, and maximal overlap wavelet distributions of EEG.

Precision-engineering of subunit vaccine particles for prevention of infectious diseases.

Vaccines remain the best approach for the prevention of infectious diseases. Protein subunit vaccines are safe compared to live-attenuated whole cell vaccines but often show reduced immunogenicity. Subunit vaccines in particulate format show improved vaccine efficacy by inducing strong immune responses leading to protective immunity against the respective pathogens.

Pre-trained ensemble model for identification of emotion during COVID-19 based on emergency response support system dataset.

The COVID-19 precautions, lockdown, and quarantine implemented throughout the epidemic resulted in a worldwide economic disaster. People are facing unprecedented levels of intense threat, necessitating professional, systematic psychiatric intervention and assistance. New psychological services must be established as quickly as possible to support the mental healthcare needs of people in this pandemic condition.

Detection of Tonic-Clonic Seizures using Wavelet Entropy of Scalp EEG.

Epilepsy is the most common chronic neurologic disorder characterized by the recurrence of unprovoked seizures. These seizures are paroxysmal events that result from abnormal neuronal discharges and are categorized into various types based on the clinical manifestations and localization. Tonic-Clonic seizures (TCSZ) may lead to injuries, and constitute the major risk factor for sudden unexpected death in epilepsy (SUDEP), especially in unattended patients.

Analysis of EEG-EMG Coherence in Low Frequency Bands.

In this, study, an attempt is made to analyze the corticomuscular coupling of the brain and muscular system in the low-frequency components during ramp descent (RD) and stair descent (SD) locomotion. For this purpose, magnitude squared coherence (MSC) is computed from the simultaneous EEG and EMG signals recorded during the ramp and stair descent tasks. The MSC is extracted from the low- frequency bands such as delta (0.