Trends in biomedical signal feature extraction

Signal analysis involves identifying signal behaviour, extracting linear and non-linear properties, compression or expansion into higher or lower dimensions, and recognizing patterns. Over the last few decades, signal processing has taken notable evolutionary leaps in terms of measurement - from being simple techniques for analysing analog or digital signals in time, frequency or joint time-frequency (TF) domain, to being complex techniques for analysis and interpretation in a higher dimensional domain. The intention behind this is simple - robust and efficient feature extraction; i.e. to identify specific signal markers or properties exhibited in one event, and use them to distinguish from characteristics exhibited in another event. The objective of our study is to give the reader a bird's eye view of the biomedical signal processing world with a zoomed-in perspective of feature extraction methodologies which form the basis of machine learning and hence, artificial intelligence. We delve into the vast world of feature extraction going across the evolutionary chain starting with basic A-to-D conversion, to domain transformations, to sparse signal representations and compressive sensing. It should be noted that in this manuscript we have attempted to explain key biomedical signal feature extraction methods in simpler fashion without detailing over mathematical representations. Additionally we have briefly touched upon the aspects of curse and blessings of signal dimensionality which would finally help us in determining the best combination of signal processing methods which could yield an efficient feature extractor. In other words, similar to how the laws of science behind some common engineering techniques are explained, in this review study we have attempted to postulate an approach towards a meaningful explanation behind those methods in developing a convincing and explainable reason as to which feature extraction method is suitable for a given biomedical signal, (C) 2018 Elsevier Ltd. All rights reserved.