Fast communication: A simple method to quantify the morphological similarity between signals

Authors:
Jie Lian;Garth Garner;Dirk Muessig;Volker Lang
Affiliations:
Micro Systems Engineering Inc., 6024 SW Jean Road, Lake Oswego, OR 97035, USA;Micro Systems Engineering Inc., 6024 SW Jean Road, Lake Oswego, OR 97035, USA;Micro Systems Engineering Inc., 6024 SW Jean Road, Lake Oswego, OR 97035, USA;Micro Systems Engineering Inc., 6024 SW Jean Road, Lake Oswego, OR 97035, USA
Venue:
Signal Processing
Year:
2010

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Abstract

We propose a simple index, termed adaptive signed correlation index (ASCI), to quantify the morphological similarity between signals. The ASCI between two signals is calculated by trichotomizing each signal based on predefined three signal subspaces, then calculating the signed correlation of the trichotomized vectors. Examples are shown to compare ASCI with conventional correlation coefficients with respect to the effects of signal perturbation and additive noise. The ASCI provides a robust and efficient measure of morphological similarity and has particular applications in embedded systems involving biological signal analysis.