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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Veröffentlicht in:	IEEE transactions on biomedical engineering Jg. 65; H. 4; S. 754 - 762
Hauptverfasser:	Yazdani, Sasan, Fallet, Sibylle, Vesin, Jean-Marc
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.04.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
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Abstract	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.
AbstractList	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. 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.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.
Author	Vesin, Jean-Marc Fallet, Sibylle Yazdani, Sasan
Author_xml	– sequence: 1 givenname: Sasan orcidid: 0000-0002-0604-1749 surname: Yazdani fullname: Yazdani, Sasan email: sasan.yazdani@epfl.ch organization: Applied Signal Processing Group, Department of Electrical Engineering, Swiss Federal Institute of Technology, Lausanne, Switzerland – sequence: 2 givenname: Sibylle surname: Fallet fullname: Fallet, Sibylle organization: Applied Signal Processing Group, Department of Electrical EngineeringSwiss Federal Institute of Technology – sequence: 3 givenname: Jean-Marc surname: Vesin fullname: Vesin, Jean-Marc organization: Applied Signal Processing Group, Department of Electrical EngineeringSwiss Federal Institute of Technology
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SubjectTerms	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
Title	A Novel Short-Term Event Extraction Algorithm for Biomedical Signals
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