English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Electromyography (EMG) signals are the measure of activity in the muscles. The aim of this study is to identify the neuromuscular disease based on EMG signals by means of classification. The neuromuscular diseases that have been identified are myopathy and neuropathy. The classification was carried out using Artificial Neural Network (ANN). There are five feature extraction techniques that were used to extract the signals such as Autoregressive (AR), Root Mean Square (RMS), Zero Crossing (ZC), Waveform length (WL) and Mean Absolute Value (MAV). A comparative analysis of these different techniques were carried out based on the results. The Multilayer Perceptron (MLP) was used for carrying out the classification

Electromyography (EMG) based Classification of Neuromuscular Disorders using Multi-Layer Perceptron