Why the study?
Automatic Machine Learning offers novel methodologies to decipher electrical biosignals used in medical diagnoses with minimal preliminary data comprehension, prompting a review of contemporary techniques.
How are Automatic Machine Learning (AutoML) techniques applied to decipher electrical biosignals in medical diagnoses?
How are Automatic Machine Learning (AutoML) techniques applied to decipher electrical biosignals in medical diagnoses?
AutoML offers novel methodologies to decipher electrical biosignals with minimal preliminary data comprehension, aiding in medical diagnoses.
May support AutoML for biosignal diagnostics; leaves open prospective validation before clinical use.
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.
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Branco et al. (2023) studied this question.
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