Automatic Machine Learning Applied to Electrical Biosignals: A Selective Review

Electrical biosignals, stemming from potential differences in specific tissues like muscles, play a pivotal role in medical diagnoses, aiding in identifying ailments from arrhythmias to brain traumas. The burgeoning realm of Automatic Machine Learning (AutoML), integrating automatic optimization with machine learning, is ushering in novel methodologies to decipher these signals with minimal preliminary data comprehension. This review delves into the contemporary discourse on AutoML techniques tailored for electrical biosignal quandaries, drawing from articles spanning 2018 to 2022 from six technology and machine learning-centric databases. Consequently, insights into biosignal comprehension, the apt AutoML solutions, and prevailing challenges in the domain are delineated.