Authors:
Rodolfo Abreu
1
;
Joana Sousa
2
and
Hugo Gamboa
3
Affiliations:
1
Universidade Nova de Lisboa, Portugal
;
2
PLUX - Wireless Biosignals S.A., Portugal
;
3
Universidade Nova de Lisboa and PLUX - Wireless Biosignals S.A., Portugal
Keyword(s):
Biosignals,Waves, Events Detection, Features Extraction, Pattern Recognition, k-Means, Parallel Computing, Signal Processing.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Detection and Identification
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Monitoring and Telemetry
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
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.