MaghrebDataLibMedV2, PubMed, Corpus, bibRecord, 000446

Real time QRS complex detection using DFA and regular grammar.

Identifieur interne : 000446 ( PubMed/Corpus ); précédent : 000445; suivant : 000447

Real time QRS complex detection using DFA and regular grammar.

Auteurs : Salah Hamdi ; Asma Ben Abdallah ; Mohamed Hedi Bedoui

Source :

Biomedical engineering online [ 1475-925X ] ; 2017.

RBID : pubmed:28241829

English descriptors

KwdEn :
- Algorithms (MeSH), Arrhythmias, Cardiac (diagnosis), Electrocardiography (MeSH), Humans (MeSH), Signal Processing, Computer-Assisted (MeSH), Signal-To-Noise Ratio (MeSH), Time Factors (MeSH).
MESH :
- diagnosis : Arrhythmias, Cardiac.
- Algorithms, Electrocardiography, Humans, Signal Processing, Computer-Assisted, Signal-To-Noise Ratio, Time Factors.

Abstract

BACKGROUND

The sequence of Q, R, and S peaks (QRS) complex detection is a crucial procedure in electrocardiogram (ECG) processing and analysis. We propose a novel approach for QRS complex detection based on the deterministic finite automata with the addition of some constraints. This paper confirms that regular grammar is useful for extracting QRS complexes and interpreting normalized ECG signals. A QRS is assimilated to a pair of adjacent peaks which meet certain criteria of standard deviation and duration.

RESULTS

The proposed method was applied on several kinds of ECG signals issued from the standard MIT-BIH arrhythmia database. A total of 48 signals were used. For an input signal, several parameters were determined, such as QRS durations, RR distances, and the peaks' amplitudes. σRR and σQRS parameters were added to quantify the regularity of RR distances and QRS durations, respectively. The sensitivity rate of the suggested method was 99.74% and the specificity rate was 99.86%. Moreover, the sensitivity and the specificity rates variations according to the Signal-to-Noise Ratio were performed.

CONCLUSIONS

Regular grammar with the addition of some constraints and deterministic automata proved functional for ECG signals diagnosis. Compared to statistical methods, the use of grammar provides satisfactory and competitive results and indices that are comparable to or even better than those cited in the literature.

DOI: 10.1186/s12938-017-0322-2
PubMed: 28241829
PubMed Central: PMC5330129

Links to Exploration step

pubmed:28241829

Le document en format XML

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<author><name sortKey="Hamdi, Salah" sort="Hamdi, Salah" uniqKey="Hamdi S" first="Salah" last="Hamdi">Salah Hamdi</name>
<affiliation><nlm:affiliation>Laboratory of Technology and Medical Imaging (LTIM), Faculty of Medicine of Monastir (FMM), University of Monastir, Monastir, Tunisia. salahxhamdi@gmail.com.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Ben Abdallah, Asma" sort="Ben Abdallah, Asma" uniqKey="Ben Abdallah A" first="Asma" last="Ben Abdallah">Asma Ben Abdallah</name>
<affiliation><nlm:affiliation>Laboratory of Technology and Medical Imaging (LTIM), Faculty of Medicine of Monastir (FMM), University of Monastir, Monastir, Tunisia.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Bedoui, Mohamed Hedi" sort="Bedoui, Mohamed Hedi" uniqKey="Bedoui M" first="Mohamed Hedi" last="Bedoui">Mohamed Hedi Bedoui</name>
<affiliation><nlm:affiliation>Laboratory of Technology and Medical Imaging (LTIM), Faculty of Medicine of Monastir (FMM), University of Monastir, Monastir, Tunisia.</nlm:affiliation>
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<author><name sortKey="Hamdi, Salah" sort="Hamdi, Salah" uniqKey="Hamdi S" first="Salah" last="Hamdi">Salah Hamdi</name>
<affiliation><nlm:affiliation>Laboratory of Technology and Medical Imaging (LTIM), Faculty of Medicine of Monastir (FMM), University of Monastir, Monastir, Tunisia. salahxhamdi@gmail.com.</nlm:affiliation>
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<author><name sortKey="Ben Abdallah, Asma" sort="Ben Abdallah, Asma" uniqKey="Ben Abdallah A" first="Asma" last="Ben Abdallah">Asma Ben Abdallah</name>
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<author><name sortKey="Bedoui, Mohamed Hedi" sort="Bedoui, Mohamed Hedi" uniqKey="Bedoui M" first="Mohamed Hedi" last="Bedoui">Mohamed Hedi Bedoui</name>
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<series><title level="j">Biomedical engineering online</title>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms (MeSH)</term>
<term>Arrhythmias, Cardiac (diagnosis)</term>
<term>Electrocardiography (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Signal Processing, Computer-Assisted (MeSH)</term>
<term>Signal-To-Noise Ratio (MeSH)</term>
<term>Time Factors (MeSH)</term>
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<keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Arrhythmias, Cardiac</term>
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<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Electrocardiography</term>
<term>Humans</term>
<term>Signal Processing, Computer-Assisted</term>
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<front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b>
</p>
<p>The sequence of Q, R, and S peaks (QRS) complex detection is a crucial procedure in electrocardiogram (ECG) processing and analysis. We propose a novel approach for QRS complex detection based on the deterministic finite automata with the addition of some constraints. This paper confirms that regular grammar is useful for extracting QRS complexes and interpreting normalized ECG signals. A QRS is assimilated to a pair of adjacent peaks which meet certain criteria of standard deviation and duration.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>RESULTS</b>
</p>
<p>The proposed method was applied on several kinds of ECG signals issued from the standard MIT-BIH arrhythmia database. A total of 48 signals were used. For an input signal, several parameters were determined, such as QRS durations, RR distances, and the peaks' amplitudes. σRR and σQRS parameters were added to quantify the regularity of RR distances and QRS durations, respectively. The sensitivity rate of the suggested method was 99.74% and the specificity rate was 99.86%. Moreover, the sensitivity and the specificity rates variations according to the Signal-to-Noise Ratio were performed.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b>
</p>
<p>Regular grammar with the addition of some constraints and deterministic automata proved functional for ECG signals diagnosis. Compared to statistical methods, the use of grammar provides satisfactory and competitive results and indices that are comparable to or even better than those cited in the literature.</p>
</div>
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<AbstractText Label="RESULTS" NlmCategory="RESULTS">The proposed method was applied on several kinds of ECG signals issued from the standard MIT-BIH arrhythmia database. A total of 48 signals were used. For an input signal, several parameters were determined, such as QRS durations, RR distances, and the peaks' amplitudes. σRR and σQRS parameters were added to quantify the regularity of RR distances and QRS durations, respectively. The sensitivity rate of the suggested method was 99.74% and the specificity rate was 99.86%. Moreover, the sensitivity and the specificity rates variations according to the Signal-to-Noise Ratio were performed.</AbstractText>
<AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Regular grammar with the addition of some constraints and deterministic automata proved functional for ECG signals diagnosis. Compared to statistical methods, the use of grammar provides satisfactory and competitive results and indices that are comparable to or even better than those cited in the literature.</AbstractText>
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<ReferenceList><Reference><Citation>Med Biol Eng Comput. 1997 Nov;35(6):747-51</Citation>
<ArticleIdList><ArticleId IdType="pubmed">9538556</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Trans Biomed Eng. 2000 Jul;47(7):849-56</Citation>
<ArticleIdList><ArticleId IdType="pubmed">10916255</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Trans Inf Technol Biomed. 2008 Sep;12(5):667-77</Citation>
<ArticleIdList><ArticleId IdType="pubmed">18779082</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Trans Biomed Eng. 1993 Feb;40(2):201-5</Citation>
<ArticleIdList><ArticleId IdType="pubmed">8319971</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Comput Methods Programs Biomed. 2000 Jun;62(2):87-98</Citation>
<ArticleIdList><ArticleId IdType="pubmed">10764935</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>J Electrocardiol. 1987 Apr;20(2):83-92</Citation>
<ArticleIdList><ArticleId IdType="pubmed">2955068</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Neural Netw. 2010 Dec;23(10):1286-99</Citation>
<ArticleIdList><ArticleId IdType="pubmed">20709495</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Trans Biomed Eng. 1999 Feb;46(2):179-85</Citation>
<ArticleIdList><ArticleId IdType="pubmed">9932339</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Trans Biomed Eng. 1990 Sep;37(9):826-36</Citation>
<ArticleIdList><ArticleId IdType="pubmed">2227969</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Trans Biomed Eng. 2009 May;56(5):1415-26</Citation>
<ArticleIdList><ArticleId IdType="pubmed">19203885</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Trans Biomed Eng. 2004 Jul;51(7):1269-73</Citation>
<ArticleIdList><ArticleId IdType="pubmed">15248543</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Med Biol Eng Comput. 1999 Sep;37(5):560-5</Citation>
<ArticleIdList><ArticleId IdType="pubmed">10723892</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Trans Biomed Eng. 1995 Jan;42(1):21-8</Citation>
<ArticleIdList><ArticleId IdType="pubmed">7851927</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Biomed Eng Online. 2004 Aug 27;3(1):28</Citation>
<ArticleIdList><ArticleId IdType="pubmed">15333132</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Trans Inf Technol Biomed. 2009 Jan;13(1):37-48</Citation>
<ArticleIdList><ArticleId IdType="pubmed">19129022</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Trans Biomed Eng. 2001 Feb;48(2):133-42</Citation>
<ArticleIdList><ArticleId IdType="pubmed">11296869</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>J Adv Res. 2013 Jul;4(4):331-44</Citation>
<ArticleIdList><ArticleId IdType="pubmed">25685438</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Trans Biomed Eng. 1986 Dec;33(12):1157-65</Citation>
<ArticleIdList><ArticleId IdType="pubmed">3817849</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Eng Med Biol Mag. 2000 Mar-Apr;19(2):95-100</Citation>
<ArticleIdList><ArticleId IdType="pubmed">10738667</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Eng Med Biol Mag. 1998 Jan-Feb;17(1):45-55</Citation>
<ArticleIdList><ArticleId IdType="pubmed">9460620</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Trans Inf Technol Biomed. 2009 Jul;13(4):512-8</Citation>
<ArticleIdList><ArticleId IdType="pubmed">19273030</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>J Med Syst. 2012 Apr;36(2):883-92</Citation>
<ArticleIdList><ArticleId IdType="pubmed">20703646</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Trans Biomed Eng. 2001 Nov;48(11):1265-71</Citation>
<ArticleIdList><ArticleId IdType="pubmed">11686625</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Trans Biomed Eng. 1985 Mar;32(3):230-6</Citation>
<ArticleIdList><ArticleId IdType="pubmed">3997178</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Trans Neural Netw. 2009 Mar;20(3):522-7</Citation>
<ArticleIdList><ArticleId IdType="pubmed">19179246</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>J Electrocardiol. 1996;29 Suppl:10-6</Citation>
<ArticleIdList><ArticleId IdType="pubmed">9238371</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Trans Biomed Eng. 1995 Nov;42(11):1137-41</Citation>
<ArticleIdList><ArticleId IdType="pubmed">7498919</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Med Biol Eng Comput. 1980 Mar;18(2):125-32</Citation>
<ArticleIdList><ArticleId IdType="pubmed">7392679</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Biomed Eng Online. 2004 Jan 29;3:3</Citation>
<ArticleIdList><ArticleId IdType="pubmed">14750981</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Eng Med Biol Mag. 2002 Jan-Feb;21(1):42-57</Citation>
<ArticleIdList><ArticleId IdType="pubmed">11935987</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>IEEE Eng Med Biol Mag. 1997 Jan-Feb;16(1):77-83</Citation>
<ArticleIdList><ArticleId IdType="pubmed">9058586</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
</PubmedData>
</pubmed>
</record>

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Real time QRS complex detection using DFA and regular grammar.

Real time QRS complex detection using DFA and regular grammar.

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Abstract

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