An EMG-based feature extraction method using a normalized weight vertical visibility algorithm for myopathy and neuropathy detection.

ABSTRACT:

Background

Electromyography (EMG) signals recorded from healthy, myopathic, and amyotrophic lateral sclerosis (ALS) subjects are nonlinear, non-stationary, and similar in the time domain and the frequency domain. Therefore, it is difficult to classify these various statuses.

Methods

This study proposes an EMG-based feature extraction method based on a normalized weight vertical visibility algorithm (NWVVA) for myopathy and ALS detection. In this method, sampling points or nodes based on sampling theory are extracted, and features are derived based on relations among the vertical visibility nodes with their amplitude differences as weights. The features are calculated via selective statistical mechanics and measurements, and the obtained features are assembled into a feature

SUBMITTER: Artameeyanant P 

PROVIDER: S-EPMC5174015 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature