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.

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.

Multifractal-based lacunarity analysis of trabecular bone in radiography.

This study presents textural characterization techniques for effective osteoporosis diagnosis using bone radiograph images. The automatic classification of osteoporosis and healthy (control) cases using bone radiograph images in this work presents a major challenge as the images show no visual differences for both cases. The proposed work utilizes multifractals to characterize the trabecular bone texture in the radiographs.