A Novel Short-Term Event Extraction Algorithm for Biomedical Signals

In this paper, we propose a fast novel nonlinear filtering method named Relative-Energy (Rel-En), for robust short-term event extraction from biomedical signals. We developed an algorithm that extracts short- and long-term energies in a signal and provides a coefficient vector with which the signal...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering Vol. 65; no. 4; pp. 754 - 762
Main Authors: Yazdani, Sasan, Fallet, Sibylle, Vesin, Jean-Marc
Format: Journal Article
Language:English
Published: United States IEEE 01.04.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Band-pass filters
Biomedical materials
Biomedical signal processing
Data processing
EEG
EKG
Electrocardiography
Electrocardiography - methods
Filtering algorithms
Heart Rate
Humans
impulse detection
non-linear signal processing
Photoplethysmography - methods
QRS-complex detection
Short term
short-term event extraction
Signal processing
Signal processing algorithms
Signal Processing, Computer-Assisted
State of the art
Wavelet transforms
ISSN:0018-9294, 1558-2531, 1558-2531
Online Access:Get full text
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Summary:In this paper, we propose a fast novel nonlinear filtering method named Relative-Energy (Rel-En), for robust short-term event extraction from biomedical signals. We developed an algorithm that extracts short- and long-term energies in a signal and provides a coefficient vector with which the signal is multiplied, heightening events of interest. This algorithm is thoroughly assessed on benchmark datasets in three different biomedical applications, namely ECG QRS-complex detection, EEG K-complex detection, and imaging photoplethysmography (iPPG) peak detection. Rel-En successfully identified the events in these settings. Compared to the state-of-the-art, better or comparable results were obtained on QRS-complex and K-complex detection. For iPPG peak detection, the proposed method was used as a preprocessing step to a fixed threshold algorithm that lead to a significant improvement in overall results. While easily defined and computed, Rel-En robustly extracted short-term events of interest. The proposed algorithm can be implemented by two filters and its parameters can be selected easily and intuitively. Furthermore, Rel-En algorithm can be used in other biomedical signal processing applications where a need of short-term event extraction is present.
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2017.2718179