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      Independent Component Analysis and Decision Trees for ECG Holter Recording De-Noising

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          Abstract

          We have developed a method focusing on ECG signal de-noising using Independent component analysis (ICA). This approach combines JADE source separation and binary decision tree for identification and subsequent ECG noise removal. In order to to test the efficiency of this method comparison to standard filtering a wavelet- based de-noising method was used. Freely data available at Physionet medical data storage were evaluated. Evaluation criteria was root mean square error (RMSE) between original ECG and filtered data contaminated with artificial noise. Proposed algorithm achieved comparable result in terms of standard noises (power line interference, base line wander, EMG), but noticeably significantly better results were achieved when uncommon noise (electrode cable movement artefact) were compared.

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          Independent component analysis of nondeterministic fMRI signal sources.

          J Jauhiainen, Juha-Heikki Kantola, Vesa Kiviniemi … (2003)
          Neuronal activation can be separated from other signal sources of functional magnetic resonance imaging (fMRI) data by using independent component analysis (ICA). Without deliberate neuronal activity of the brain cortex, the fMRI signal is a stochastic sum of various physiological and artifact related signal sources. The ability of spatial-domain ICA to separate spontaneous physiological signal sources was evaluated in 15 anesthetized children known to present prominent vasomotor fluctuations in the functional cortices. ICA separated multiple clustered signal sources in the primary sensory areas in all of the subjects. The spatial distribution and frequency spectra of the signal sources correspond to the known properties of 0.03-Hz very-low-frequency vasomotor waves in fMRI data. In addition, ICA was able to separate major artery and sagittal sinus related signal sources in each subject. The characteristics of the blood vessel related signal sources were different from the parenchyma sources. ICA analysis of fMRI can be used for both assessing the statistical independence of brain signals and segmenting nondeterministic signal sources for further analysis.
          Article 0 comments Cited 96 times – based on 0 reviews     Review now
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            The European ST-T database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography.

            M Emdin, G Distante, C. Marchesi … (1992)
            The project for the development of the European ST-T annotated Database originated from a 'Concerted Action' on Ambulatory Monitoring, set up by the European Community in 1985. The goal was to prototype an ECG database for assessing the quality of ambulatory ECG monitoring (AECG) systems. After the 'concerted action', the development of the full database was coordinated by the Institute of Clinical Physiology of the National Research Council (CNR) in Pisa and the Thoraxcenter of Erasmus University in Rotterdam. Thirteen research groups from eight countries provided AECG tapes and annotated beat by beat the selected 2-channel records, each 2 h in duration. ST segment (ST) and T-wave (T) changes were identified and their onset, offset and peak beats annotated in addition to QRSs, beat types, rhythm and signal quality changes. In 1989, the European Society of Cardiology sponsored the remainder of the project. Recently the 90 records were completed and stored on CD-ROM. The records include 372 ST and 423 T changes. In cooperation with the Biomedical Engineering Centre of MIT (developers of the MIT-BIH arrhythmia database), the annotation scheme was revised to be consistent with both MIT-BIH and American Heart Association formats.
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              Long-term ST database: a reference for the development and evaluation of automated ischaemia detectors and for the study of the dynamics of myocardial ischaemia.

              José Taddei, C. Marchesi, G Antolic … (2003)
              The long-term ST database is the result of a multinational research effort. The goal was to develop a challenging and realistic research resource for development and evaluation of automated systems to detect transient ST segment changes in electrocardiograms and for supporting basic research into the mechanisms and dynamics of transient myocardial ischaemia. Twenty-four hour ambulatory ECG records were selected from routine clinical practice settings in the USA and Europe, between 1994 and 2000, on the basis of occurrence of ischaemic and non-ischaemic ST segment changes. Human expert annotators used newly developed annotation protocols and a specially developed interactive graphic editor tool (SEMIA) that supported paperless editing of annotations and facilitated international co-operation via the Internet. The database contains 86 two- and three-channel 24 h annotated ambulatory records from 80 patients and is stored on DVD-ROMs. The database annotation files contain ST segment annotations of transient ischaemic (1155) and heart-rate related ST episodes and annotations of non-ischaemic ST segment events related to postural changes and conduction abnormalities. The database is intended to complement the European Society of Cardiology ST-T database and the MIT-BIH and AHA arrhythmia databases. It provides a comprehensive representation of 'real-world' data, with numerous examples of transient ischaemic and non-ischaemic ST segment changes, arrhythmias, conduction abnormalities, axis shifts, noise and artifacts.
              Article 0 comments Cited 27 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Dewen Hu: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2014
                Publication date (Electronic): 6 June 2014
                Volume: 9
                Issue: 6
                Electronic Location Identifier: e98450
                Affiliations
                [1 ]Department of Cybernetics, FEE, CTU in Prague, Prague, Czech Republic
                [2 ]Czech Institute of Informatics, Robotics, and Cybernetics, CTU in Prague, Prague, Czech Republic
                [3 ]Department of Cardiovascular Diseases, ICRC, St. Anne's Hospital in Brno, Brno, Czech Republic
                College of Mechatronics and Automation, National University of Defense Technology, China
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: JK VK LL. Performed the experiments: JK. Analyzed the data: JK VK FS. Contributed reagents/materials/analysis tools: JK. Wrote the paper: JK VK FS LL.

                Article
                Publisher ID: PONE-D-13-47704
                DOI: 10.1371/journal.pone.0098450
                PMC ID: 4048160
                PubMed ID: 24905359
                SO-VID: 62c15c75-1972-4696-8556-4e3dc1df2a61
                Copyright statement: Copyright @ 2014
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 13 November 2013
                Date accepted : 3 May 2014
                Page count
                Pages: 9
                Funding
                This work was supported by post doctoral research project by Czech Science Foundation GACR #P103/11/P106 and by the CTU Grant SGS13/203/OHK3/3T/13. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article
                Subject: Biology and Life Sciences
                Subject: Biotechnology
                Subject: Bioengineering
                Subject: Biomedical Engineering
                Subject: Engineering and Technology
                Subject: Signal Processing
                Subject: Signal Filtering
                Subject: Statistical Signal Processing
                Subject: Medicine and Health Sciences
                Subject: Cardiology
                Subject: Diagnostic Medicine
                Subject: Diagnostic Radiology
                Subject: Radiology and Imaging
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Applied Mathematics
                Subject: Algorithms
                Subject: Decision Theory
                Subject: Discrete Mathematics
                Subject: Computational Systems

                ScienceOpen disciplines: Uncategorized
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                ScienceOpen disciplines: Uncategorized

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