Transformers in biosignal analysis: A review

Abstract

Transformer architectures have become increasingly popular in healthcare applications. Through outstanding performance in natural language processing and superior capability to encode sequences, transformers have influenced researchers from various healthcare domains. Biosignal processing, in particular, has been a main focus in healthcare research to understand and assess complex physiological processes. Since their advent, multiple variants of transformer architectures have been leveraged by numerous studies to classify, analyze, and extract physiological events encoded within biosignals. In this paper, we aim to conduct a comprehensive survey that bridges research endeavors and highlights the most common and state-of-the-art transformer architectures utilized across the various subfields of biosignal analysis. Additionally, we also provide an objective comparison between transformers and similar sequence-specialized neural networks to highlight strengths, weaknesses, and best practices in biosignal analysis. By doing so, we aspire to provide a roadmap for researchers interested in leveraging transformer architectures for biosignal analysis applications.