Abstract

The present study aims to assess the knowledge, attitude, and experience of off-label prescribing practice among physicians in Baghdad city hospitals. This cross-sectional study was performed through the period from November 1^st 2018 to March 2019 at 17 hospitals, a self-administered questionnaire was utilized to collect data from the physicians, and the targeted hospitals were randomly selected at different regions in Baghdad City area. Out of the 400 distributed questionnaires to the physicians, 383 of them were returned completed, 57.2% indicated that they were reasonably familiar with the term “off label drug”, 57.7% mentioned that the most common medical reasons for the prescribing o

Amputation of the upper limb significantly hinders the ability of patients to perform activities of daily living. To address this challenge, this paper introduces a novel approach that combines non-invasive methods, specifically Electroencephalography (EEG) and Electromyography (EMG) signals, with advanced machine learning techniques to recognize upper limb movements. The objective is to improve the control and functionality of prosthetic upper limbs through effective pattern recognition. The proposed methodology involves the fusion of EMG and EEG signals, which are processed using time-frequency domain feature extraction techniques. This enables the classification of seven distinct hand and wrist movements. The experiments conducte