Classificação de sinais eletromiográficos para auxílio ao diagnóstico médico

Abstract

A study published in 2016 in the journal Improving Diagnosis in Health Care by the Institute of Medicine (IMO) states that one in ten diagnoses are incorrect and that at least one person in three has experience related to a diagnostic error. Researchers have found that errors in diagnoses account for the largest fraction of negligence claims and the highest amount of penalty payments such as fines and damages. From the clinical point of view, the diagnosis of diseases such as Amyotrophic Lateral Sclerosis (ALS) and myopathies in the early stages of diagnosis is difficult to diagnose, as the symptoms of these diseases can easily be confused with other diseases. However, this work presents a hybrid system to aid in the diagnosis (CAD), aiming at improving and increasing the efficiency, accuracy and speed of the clinical diagnosis, capable of automatically classifying electromyographic signals into healthy, myopathy and ELA, providing a second opinion to the medical specialist.