Effective electromyographic (EMG) signal characterization is critical in the diagnosis of
neuromuscular disorders. Machine-learning based pattern classification algorithms are …

Objective This scoping review provides an overview of artificial intelligence (AI), including
machine and deep learning techniques, in the interpretation of clinical needle …

In electrophysiological hearing assessment and diagnosis of brain stem lesions are most
often used auditory brainstem evoked potentials of short latency. They are characterized by …

Advanced technologies for muscle tracking provide easy access to identify and track muscle
activity, often for the purposes of therapeutic interventions. The necessity of muscle trackers …

Although a well-established body of literature has examined electrophysiological muscle
classification methods and systems, ways to enhance their transparency is still an important …

Electrophysiological muscle classification involves characterization of extracted motor unit
potentials (MUPs) followed by the aggregation of these MUP characterizations. Existing …

This study examined the characteristics of lower limb muscle activity in elderly persons after
ergometric pedaling exercise for 1 month. To determine the effect of the exercise, surface …

Objective: Electrophysiological muscle classification (EMC) is a crucial step in the diagnosis
of neuromuscular disorders. Existing quantitative techniques are not sufficiently robust and …

Anders Fuglsang-Frederiksen, Kirsten Pugdahl, and Hatice Tankisi spikes and amplitude. At
weak effort one can identify individual MUPs, while at higher efforts there is interference with …

Neuromuscular disorder is a broad term that refers to diseases that impair muscle
functionality either by affecting any part of the nerve or muscle. Electrodiagnosis of most …