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
Hauptverfasser: Molina-Picó, Antonio, Cuesta-Frau, David, Aboy, Mateo, Crespo, Cristina, Miró-Martínez, Pau, Oltra-Crespo, Sandra
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container_end_page 106
container_issue 2
container_start_page 97
container_title Artificial intelligence in medicine
container_volume 53
creator Molina-Picó, Antonio
Cuesta-Frau, David
Aboy, Mateo
Crespo, Cristina
Miró-Martínez, Pau
Oltra-Crespo, Sandra
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. 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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. 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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. 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source MEDLINE; Elsevier ScienceDirect Journals
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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