Biometric and emotion identification: an ECG compression based method

Detalhes bibliográficos
Autor(a) principal: Brás, Susana
Data de Publicação: 2018
Outros Autores: Ferreira, Jacqueline H. T., Soares, Sandra C., Pinho, Armando J.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/27613
Resumo: We present an innovative and robust solution to both biometric and emotion identification using the electrocardiogram (ECG). The ECG represents the electrical signal that comes from the contraction of the heart muscles, indirectly representing the flow of blood inside the heart, it is known to convey a key that allows biometric identification. Moreover, due to its relationship with the nervous system, it also varies as a function of the emotional state. The use of information-theoretic data models, associated with data compression algorithms, allowed to effectively compare ECG records and infer the person identity, as well as emotional state at the time of data collection. The proposed method does not require ECG wave delineation or alignment, which reduces preprocessing error. The method is divided into three steps: (1) conversion of the real-valued ECG record into a symbolic time-series, using a quantization process; (2) conditional compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. We obtained over 98% of accuracy in biometric identification, whereas in emotion recognition we attained over 90%. Therefore, the method adequately identify the person, and his/her emotion. Also, the proposed method is flexible and may be adapted to different problems, by the alteration of the templates for training the model.
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spelling Biometric and emotion identification: an ECG compression based methodBiometricsEmotionQuantizationData compressionKolmogorov complexityWe present an innovative and robust solution to both biometric and emotion identification using the electrocardiogram (ECG). The ECG represents the electrical signal that comes from the contraction of the heart muscles, indirectly representing the flow of blood inside the heart, it is known to convey a key that allows biometric identification. Moreover, due to its relationship with the nervous system, it also varies as a function of the emotional state. The use of information-theoretic data models, associated with data compression algorithms, allowed to effectively compare ECG records and infer the person identity, as well as emotional state at the time of data collection. The proposed method does not require ECG wave delineation or alignment, which reduces preprocessing error. The method is divided into three steps: (1) conversion of the real-valued ECG record into a symbolic time-series, using a quantization process; (2) conditional compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. We obtained over 98% of accuracy in biometric identification, whereas in emotion recognition we attained over 90%. Therefore, the method adequately identify the person, and his/her emotion. Also, the proposed method is flexible and may be adapted to different problems, by the alteration of the templates for training the model.Frontiers Media2020-02-20T11:46:54Z2018-04-04T00:00:00Z2018-04-04info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/27613eng10.3389/fpsyg.2018.00467Brás, SusanaFerreira, Jacqueline H. T.Soares, Sandra C.Pinho, Armando J.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T11:53:29Zoai:ria.ua.pt:10773/27613Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:00:20.636395Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Biometric and emotion identification: an ECG compression based method
title Biometric and emotion identification: an ECG compression based method
spellingShingle Biometric and emotion identification: an ECG compression based method
Brás, Susana
Biometrics
Emotion
Quantization
Data compression
Kolmogorov complexity
title_short Biometric and emotion identification: an ECG compression based method
title_full Biometric and emotion identification: an ECG compression based method
title_fullStr Biometric and emotion identification: an ECG compression based method
title_full_unstemmed Biometric and emotion identification: an ECG compression based method
title_sort Biometric and emotion identification: an ECG compression based method
author Brás, Susana
author_facet Brás, Susana
Ferreira, Jacqueline H. T.
Soares, Sandra C.
Pinho, Armando J.
author_role author
author2 Ferreira, Jacqueline H. T.
Soares, Sandra C.
Pinho, Armando J.
author2_role author
author
author
dc.contributor.author.fl_str_mv Brás, Susana
Ferreira, Jacqueline H. T.
Soares, Sandra C.
Pinho, Armando J.
dc.subject.por.fl_str_mv Biometrics
Emotion
Quantization
Data compression
Kolmogorov complexity
topic Biometrics
Emotion
Quantization
Data compression
Kolmogorov complexity
description We present an innovative and robust solution to both biometric and emotion identification using the electrocardiogram (ECG). The ECG represents the electrical signal that comes from the contraction of the heart muscles, indirectly representing the flow of blood inside the heart, it is known to convey a key that allows biometric identification. Moreover, due to its relationship with the nervous system, it also varies as a function of the emotional state. The use of information-theoretic data models, associated with data compression algorithms, allowed to effectively compare ECG records and infer the person identity, as well as emotional state at the time of data collection. The proposed method does not require ECG wave delineation or alignment, which reduces preprocessing error. The method is divided into three steps: (1) conversion of the real-valued ECG record into a symbolic time-series, using a quantization process; (2) conditional compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. We obtained over 98% of accuracy in biometric identification, whereas in emotion recognition we attained over 90%. Therefore, the method adequately identify the person, and his/her emotion. Also, the proposed method is flexible and may be adapted to different problems, by the alteration of the templates for training the model.
publishDate 2018
dc.date.none.fl_str_mv 2018-04-04T00:00:00Z
2018-04-04
2020-02-20T11:46:54Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/27613
url http://hdl.handle.net/10773/27613
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.3389/fpsyg.2018.00467
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dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
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instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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