Comparative study of approximate entropy and sample entropy robustness to spikes
Abstract Objective There is an ongoing research effort devoted to characterize the signal regularity metrics approximate entropy (ApEn) and sample entropy (SampEn) in order to better interpret their results in the context of biomedical signal analysis. Along with this line, this paper addresses the...
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Veröffentlicht in: | Artificial intelligence in medicine 2011-10, Vol.53 (2), p.97-106 |
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description | Abstract Objective There is an ongoing research effort devoted to characterize the signal regularity metrics approximate entropy (ApEn) and sample entropy (SampEn) in order to better interpret their results in the context of biomedical signal analysis. Along with this line, this paper addresses the influence of abnormal spikes (impulses) on ApEn and SampEn measurements. Methods A set of test signals consisting of generic synthetic signals, simulated biomedical signals, and real RR records was created. These test signals were corrupted by randomly generated spikes. ApEn and SampEn were computed for all the signals under different spike probabilities and for 100 realizations. Results The effect of the presence of spikes on ApEn and SampEn is different for test signals with narrowband line spectra and test signals that are better modeled as broadband random processes. In the first case, the presence of extrinsic spikes in the signal results in an ApEn and SampEn increase. In the second case, it results in an entropy decrease. For real RR records, the presence of spikes, often due to QRS detection errors, also results in an entropy decrease. Conclusions Our findings demonstrate that both ApEn and SampEn are very sensitive to the presence of spikes. Abnormal spikes should be removed, if possible, from signals before computing ApEn or SampEn. Otherwise, the results can lead to misunderstandings or misclassification of the signal regularity. |
doi_str_mv | 10.1016/j.artmed.2011.06.007 |
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Along with this line, this paper addresses the influence of abnormal spikes (impulses) on ApEn and SampEn measurements. Methods A set of test signals consisting of generic synthetic signals, simulated biomedical signals, and real RR records was created. These test signals were corrupted by randomly generated spikes. ApEn and SampEn were computed for all the signals under different spike probabilities and for 100 realizations. Results The effect of the presence of spikes on ApEn and SampEn is different for test signals with narrowband line spectra and test signals that are better modeled as broadband random processes. In the first case, the presence of extrinsic spikes in the signal results in an ApEn and SampEn increase. In the second case, it results in an entropy decrease. For real RR records, the presence of spikes, often due to QRS detection errors, also results in an entropy decrease. Conclusions Our findings demonstrate that both ApEn and SampEn are very sensitive to the presence of spikes. Abnormal spikes should be removed, if possible, from signals before computing ApEn or SampEn. Otherwise, the results can lead to misunderstandings or misclassification of the signal regularity.</description><identifier>ISSN: 0933-3657</identifier><identifier>EISSN: 1873-2860</identifier><identifier>DOI: 10.1016/j.artmed.2011.06.007</identifier><identifier>PMID: 21835600</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Algorithms ; Approximate entropy characterization ; Electrocardiography ; Entropy ; Humans ; Internal Medicine ; Other ; RR interval record classification ; Sample entropy characterization ; Signal Processing, Computer-Assisted ; Signal spikes ; Stochastic Processes</subject><ispartof>Artificial intelligence in medicine, 2011-10, Vol.53 (2), p.97-106</ispartof><rights>Elsevier B.V.</rights><rights>2011 Elsevier B.V.</rights><rights>Copyright © 2011 Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c560t-9b803d564722be2e4c1e6c340fc656b9a324a45c480761e1b1bc81df696b16bd3</citedby><cites>FETCH-LOGICAL-c560t-9b803d564722be2e4c1e6c340fc656b9a324a45c480761e1b1bc81df696b16bd3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.artmed.2011.06.007$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,45974</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21835600$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Molina-Picó, Antonio</creatorcontrib><creatorcontrib>Cuesta-Frau, David</creatorcontrib><creatorcontrib>Aboy, Mateo</creatorcontrib><creatorcontrib>Crespo, Cristina</creatorcontrib><creatorcontrib>Miró-Martínez, Pau</creatorcontrib><creatorcontrib>Oltra-Crespo, Sandra</creatorcontrib><title>Comparative study of approximate entropy and sample entropy robustness to spikes</title><title>Artificial intelligence in medicine</title><addtitle>Artif Intell Med</addtitle><description>Abstract Objective There is an ongoing research effort devoted to characterize the signal regularity metrics approximate entropy (ApEn) and sample entropy (SampEn) in order to better interpret their results in the context of biomedical signal analysis. Along with this line, this paper addresses the influence of abnormal spikes (impulses) on ApEn and SampEn measurements. Methods A set of test signals consisting of generic synthetic signals, simulated biomedical signals, and real RR records was created. These test signals were corrupted by randomly generated spikes. ApEn and SampEn were computed for all the signals under different spike probabilities and for 100 realizations. Results The effect of the presence of spikes on ApEn and SampEn is different for test signals with narrowband line spectra and test signals that are better modeled as broadband random processes. In the first case, the presence of extrinsic spikes in the signal results in an ApEn and SampEn increase. In the second case, it results in an entropy decrease. For real RR records, the presence of spikes, often due to QRS detection errors, also results in an entropy decrease. Conclusions Our findings demonstrate that both ApEn and SampEn are very sensitive to the presence of spikes. Abnormal spikes should be removed, if possible, from signals before computing ApEn or SampEn. Otherwise, the results can lead to misunderstandings or misclassification of the signal regularity.</description><subject>Algorithms</subject><subject>Approximate entropy characterization</subject><subject>Electrocardiography</subject><subject>Entropy</subject><subject>Humans</subject><subject>Internal Medicine</subject><subject>Other</subject><subject>RR interval record classification</subject><subject>Sample entropy characterization</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Signal spikes</subject><subject>Stochastic Processes</subject><issn>0933-3657</issn><issn>1873-2860</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkU2L1TAUhoMoznX0H4h056o1J0nTdCPIxS8YUFDXIUlPIXfapibp4P33ptwZBDezCiTPOW94XkJeA22Agnx3akzMMw4NowANlQ2l3RNyANXxmilJn5ID7TmvuWy7K_IipRMthAD5nFwxULyVlB7I92OYVxNN9ndYpbwN5yqMlVnXGP742WSscMkxrOfKLEOVzLxO_65isFvKC6ZU5VCl1d9iekmejWZK-Or-vCa_Pn38efxS33z7_PX44aZ2JTjXvVWUD60UHWMWGQoHKB0XdHSylbY3nAkjWicU7SQgWLBOwTDKXlqQduDX5O1lb_np7w1T1rNPDqfJLBi2pHugUsqWsUdJpRQX0IqdFBfSxZBSxFGvsUiIZw1U79L1SV-k6126plIXpWXszX3AZve3h6EHywV4fwGwCLnzGHVyHheHg4_osh6Cfyzh_wVu8ot3ZrrFM6ZT2OJSZGvQiWmqf-zF770D7J13Hf8LRQOp_Q</recordid><startdate>20111001</startdate><enddate>20111001</enddate><creator>Molina-Picó, Antonio</creator><creator>Cuesta-Frau, David</creator><creator>Aboy, Mateo</creator><creator>Crespo, Cristina</creator><creator>Miró-Martínez, Pau</creator><creator>Oltra-Crespo, Sandra</creator><general>Elsevier B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>20111001</creationdate><title>Comparative study of approximate entropy and sample entropy robustness to spikes</title><author>Molina-Picó, Antonio ; Cuesta-Frau, David ; Aboy, Mateo ; Crespo, Cristina ; Miró-Martínez, Pau ; Oltra-Crespo, Sandra</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c560t-9b803d564722be2e4c1e6c340fc656b9a324a45c480761e1b1bc81df696b16bd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Algorithms</topic><topic>Approximate entropy characterization</topic><topic>Electrocardiography</topic><topic>Entropy</topic><topic>Humans</topic><topic>Internal Medicine</topic><topic>Other</topic><topic>RR interval record classification</topic><topic>Sample entropy characterization</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Signal spikes</topic><topic>Stochastic Processes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Molina-Picó, Antonio</creatorcontrib><creatorcontrib>Cuesta-Frau, David</creatorcontrib><creatorcontrib>Aboy, Mateo</creatorcontrib><creatorcontrib>Crespo, Cristina</creatorcontrib><creatorcontrib>Miró-Martínez, Pau</creatorcontrib><creatorcontrib>Oltra-Crespo, Sandra</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Artificial intelligence in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Molina-Picó, Antonio</au><au>Cuesta-Frau, David</au><au>Aboy, Mateo</au><au>Crespo, Cristina</au><au>Miró-Martínez, Pau</au><au>Oltra-Crespo, Sandra</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparative study of approximate entropy and sample entropy robustness to spikes</atitle><jtitle>Artificial intelligence in medicine</jtitle><addtitle>Artif Intell Med</addtitle><date>2011-10-01</date><risdate>2011</risdate><volume>53</volume><issue>2</issue><spage>97</spage><epage>106</epage><pages>97-106</pages><issn>0933-3657</issn><eissn>1873-2860</eissn><abstract>Abstract Objective There is an ongoing research effort devoted to characterize the signal regularity metrics approximate entropy (ApEn) and sample entropy (SampEn) in order to better interpret their results in the context of biomedical signal analysis. Along with this line, this paper addresses the influence of abnormal spikes (impulses) on ApEn and SampEn measurements. Methods A set of test signals consisting of generic synthetic signals, simulated biomedical signals, and real RR records was created. These test signals were corrupted by randomly generated spikes. ApEn and SampEn were computed for all the signals under different spike probabilities and for 100 realizations. Results The effect of the presence of spikes on ApEn and SampEn is different for test signals with narrowband line spectra and test signals that are better modeled as broadband random processes. In the first case, the presence of extrinsic spikes in the signal results in an ApEn and SampEn increase. In the second case, it results in an entropy decrease. For real RR records, the presence of spikes, often due to QRS detection errors, also results in an entropy decrease. Conclusions Our findings demonstrate that both ApEn and SampEn are very sensitive to the presence of spikes. Abnormal spikes should be removed, if possible, from signals before computing ApEn or SampEn. Otherwise, the results can lead to misunderstandings or misclassification of the signal regularity.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>21835600</pmid><doi>10.1016/j.artmed.2011.06.007</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Approximate entropy characterization Electrocardiography Entropy Humans Internal Medicine Other RR interval record classification Sample entropy characterization Signal Processing, Computer-Assisted Signal spikes Stochastic Processes |
title | Comparative study of approximate entropy and sample entropy robustness to spikes |
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