Electromyographic data recordings during isometric contractions of the biceps brachii and deltoid collected from patients and healthy subjects.

Electromyography (EMG) is a valuable tool for exploring both the nervous and muscular system. Two forms of pathologies called myopathy and neuropathy can affect the neuromuscular system. Myopathy refers to muscle conditions where muscle fibers show dysfunction while neuropathy is a neurological disorder related to the degeneration of nerves, resulting in muscle weakness, cramps, stiffness, muscle wasting, etc. Therefore, needle electromyography, known as intramuscular EMG (iEMG), is mainly used in clinical practice to diagnose patients with neuromuscular disorders. The dataset presented in this article contains three classes of raw invasive EMG signals, recorded from two nature of muscles, biceps brachii and deltoid, this data was collected from 241 participants, in hospital environment, spanning various ages and genders, 191 patients afflicted with either neuropathy or myopathy disease and 50 healthy subjects during isometric contraction. Furthermore, a Matlab code is provided to facilitate the reading of EMG files, carry out resampling and filtering procedures, and eliminate the 50 Hz noise component.
This dataset aimed to compare healthy and non-healthy subjects. Therefore, it can be useful as a reference to build an artificial intelligence model or validate a machine learning models created with different datasets, for the early detection of neuromuscular pathologies.
Experimental dataset is considered of raw invasive EMG signals, this dataset were collected from October 2015 to April 2019 at the Neurology Department of Mustapha Pacha University Hospital of Algiers. These signals were collected with collaboration and support of specialist clinicians throughout the process and in partnership with the Spoken Communication and Signal Processing Laboratory and Instrumentation Laboratory of the Faculty of Electronics and Informatics of Houari Boumediene University of Sciences and Technology.
This EMG dataset covers a large population of healthy people in very good shape and sick subjects of various ages, genders, and neuromuscular pathologies like myopathy (i.e., myotonia and polymyositis) and neuropathy (i.e., anterior horn and multiple sclerosis and radicular damage), showing different severity level of disease