What is IPUS and how does it help resolve biosignal complexity?

Abstract

Integrated Processing and Understanding of Signals (IPUS) combines signal processing and artificial intelligence approaches to develop algorithms for resolving signal complexity. It has also led to development over the last decade and a half of software tools for supporting the algorithm design process. The signals to be analyzed are the superposition of temporally localized and temporally overlapping signal components from broadly defined signal classes pertinent to the given application. Resolving a signal's complexity thus amounts to "decoding" it to reveal details of the specific signal components that are present at each point of a dense temporal grid defined on the signal. IPUS uses artificial intelligence techniques such as rule-based inference in conjunction with parameterized signal processing transformations to combat the combinatorial explosion encountered in any exhaustive search among the possible decoding answers for a given signal. Originally developed in the mid 1990's for auditory scene analysis, the IPUS approach has since been refined and extended in the context of various applications. In this paper, we present an overview of IPUS and discuss why its latest developments significantly impact biosignal analysis in diverse rehabilitation applications.