A signal-independent algorithm for information extraction and signal annotation of long-term records

One of the biggest challenges when analysing data is to extract information from it. In this study, we present a signal-independent algorithm that detects events on biosignals and extracts information from them by applying a new parallel version of the k-means clustering algorithm. Events can be found using a peaks detection algorithm that uses the signal RMS as an adaptive threshold or by morphological analysis through the computation of the signal meanwave. Different types of signals were acquired and annotated by the presented algorithm. By visual inspection, we obtained an accuracy of 97.7% and 97.5% using the L₁ and L₂ Minkowski distances, respectively, as distance functions and 97.6% using the meanwave distance. The fact that this algorithm can be applied to long-term raw biosignals and without requiring any prior information about them makes it an important contribution in biosignals information extraction and annotation.