Rényi and permutation entropy analysis for assessment of cardiac autonomic neuropathy

C. Carricarte-Naranjo, D. J. Cornforth, L. M. Sanchez-Rodriguez, Marta Brown, M. Estévez, A. Machado, Herbert F. Jelinek

Research output: Book chapter/Published conference paperConference paper

Abstract

Cardiac autonomic neuropathy (CAN) is a complication of diabetes with a long asymptomatic phase that is associated with high morbidity and mortality. Early identification of CAN in Type 1 diabetes mellitus (T1DM) may be possible using heart rate variability (HRV). However, the power of HRV analysis to identify CAN depends on the selection of suitable features that provide reliable information regarding cardiac autonomic regulation. Our aim was to compare the performance of Rényi entropy (RE) and permutation entropy (PE) for identification of T1DM patients with CAN. RE and PE measures from 235 data points and 5 min of cardiac interbeat interval (RR) sequences were analysed in 18 T1DM patients without CAN, 14 T1DM patients with CAN, and healthy controls matched for age and sex. RE was calculated for different orders α (-5, 5), pattern lengths λ (2, 4, 8), and tolerance σ. For PE analysis λ was set to (3-4) and time delays τ to (1-10). A forward stepwise discriminant analysis was carried out for estimating the classification functions. Accuracy was estimated following a K-fold cross-validation (k = 14). RE calculated for RR sequences of λ = 2, α > 0 showed the best performance for differentiating T1DM patients with CAN (p < 0.0001). PE measures showed better performance with ordinal patterns and τ = 4, 5 and 7 for differentiating patients with CAN. RE and PE provide complementary information achieving 100% classification accuracy (p < 0.0001 and p < 0.001, respectively). This approach might be promising as a sensitive and specific tool for CAN diagnosis in T1DM.
LanguageEnglish
Title of book or conference publicationEMBEC and NBC 2017
Subtitle of book or conference publication Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics
EditorsHannu Eskola, Outi Vaisanen, Jari Viik, Jari Hyttinen
PublisherSpringer-Verlag London Ltd.
Pages755-758
Number of pages4
Volume65
ISBN (Electronic)9789811051227
ISBN (Print)9789811051210
DOIs
Publication statusPublished - 2018
EventEMBEC & NBC 2017: Joint Conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC) - Tampere Hall, Tampere, Finland
Duration: 11 Jun 201715 Jun 2017
https://embec2017.org/ (Conference website)
https://link.springer.com/book/10.1007/978-981-10-5122-7 (Conference proceedings)

Publication series

NameIFMBE Proceedings
PublisherSpringer Nature
Volume65
ISSN (Print)1680-0737
ISSN (Electronic)1433-9277

Conference

ConferenceEMBEC & NBC 2017
CountryFinland
CityTampere
Period11/06/1715/06/17
OtherIt is our pleasure to invite you to the joint conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC), in Tampere, Finland, in June 2017. These two long running conferences are now combined for the first time with aim to build a truly cross-discipline conference. We aim to present all traditional BMES and BME areas, but also highlight new emerging fields, such as tissue engineering, bioinformatics, biosensing, neurotechnology, additive manufacturing technologies for medicine and biology, and bioimaging, to name a few. Moreover, we will emphasize the role of education, translational research, and commercialization.The scientific and social program at EMBEC’17 & NBC’17 provides an excellent platform for engineers, physicists, biologists, and clinical experts to enhance our knowledge and scientific achievements by bridging complementary disciplines and new findings into an interactive and attractive forum.
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Entropy
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Cite this

Carricarte-Naranjo, C., Cornforth, D. J., Sanchez-Rodriguez, L. M., Brown, M., Estévez, M., Machado, A., & Jelinek, H. F. (2018). Rényi and permutation entropy analysis for assessment of cardiac autonomic neuropathy. In H. Eskola, O. Vaisanen, J. Viik, & J. Hyttinen (Eds.), EMBEC and NBC 2017: Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (Vol. 65, pp. 755-758). (IFMBE Proceedings; Vol. 65). Springer-Verlag London Ltd.. https://doi.org/10.1007/978-981-10-5122-7_189
Carricarte-Naranjo, C. ; Cornforth, D. J. ; Sanchez-Rodriguez, L. M. ; Brown, Marta ; Estévez, M. ; Machado, A. ; Jelinek, Herbert F. / Rényi and permutation entropy analysis for assessment of cardiac autonomic neuropathy. EMBEC and NBC 2017: Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics. editor / Hannu Eskola ; Outi Vaisanen ; Jari Viik ; Jari Hyttinen. Vol. 65 Springer-Verlag London Ltd., 2018. pp. 755-758 (IFMBE Proceedings).
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title = "R{\'e}nyi and permutation entropy analysis for assessment of cardiac autonomic neuropathy",
abstract = "Cardiac autonomic neuropathy (CAN) is a complication of diabetes with a long asymptomatic phase that is associated with high morbidity and mortality. Early identification of CAN in Type 1 diabetes mellitus (T1DM) may be possible using heart rate variability (HRV). However, the power of HRV analysis to identify CAN depends on the selection of suitable features that provide reliable information regarding cardiac autonomic regulation. Our aim was to compare the performance of R{\'e}nyi entropy (RE) and permutation entropy (PE) for identification of T1DM patients with CAN. RE and PE measures from 235 data points and 5 min of cardiac interbeat interval (RR) sequences were analysed in 18 T1DM patients without CAN, 14 T1DM patients with CAN, and healthy controls matched for age and sex. RE was calculated for different orders α (-5, 5), pattern lengths λ (2, 4, 8), and tolerance σ. For PE analysis λ was set to (3-4) and time delays τ to (1-10). A forward stepwise discriminant analysis was carried out for estimating the classification functions. Accuracy was estimated following a K-fold cross-validation (k = 14). RE calculated for RR sequences of λ = 2, α > 0 showed the best performance for differentiating T1DM patients with CAN (p < 0.0001). PE measures showed better performance with ordinal patterns and τ = 4, 5 and 7 for differentiating patients with CAN. RE and PE provide complementary information achieving 100{\%} classification accuracy (p < 0.0001 and p < 0.001, respectively). This approach might be promising as a sensitive and specific tool for CAN diagnosis in T1DM.",
keywords = "Cardiac autonomic neuropathy, Heart rate variability, Ordinal patterns, Permutation entropy, R{\'e}nyi entropy",
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Carricarte-Naranjo, C, Cornforth, DJ, Sanchez-Rodriguez, LM, Brown, M, Estévez, M, Machado, A & Jelinek, HF 2018, Rényi and permutation entropy analysis for assessment of cardiac autonomic neuropathy. in H Eskola, O Vaisanen, J Viik & J Hyttinen (eds), EMBEC and NBC 2017: Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics. vol. 65, IFMBE Proceedings, vol. 65, Springer-Verlag London Ltd., pp. 755-758, EMBEC & NBC 2017, Tampere, Finland, 11/06/17. https://doi.org/10.1007/978-981-10-5122-7_189

Rényi and permutation entropy analysis for assessment of cardiac autonomic neuropathy. / Carricarte-Naranjo, C.; Cornforth, D. J.; Sanchez-Rodriguez, L. M.; Brown, Marta; Estévez, M.; Machado, A.; Jelinek, Herbert F.

EMBEC and NBC 2017: Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics. ed. / Hannu Eskola; Outi Vaisanen; Jari Viik; Jari Hyttinen. Vol. 65 Springer-Verlag London Ltd., 2018. p. 755-758 (IFMBE Proceedings; Vol. 65).

Research output: Book chapter/Published conference paperConference paper

TY - GEN

T1 - Rényi and permutation entropy analysis for assessment of cardiac autonomic neuropathy

AU - Carricarte-Naranjo, C.

AU - Cornforth, D. J.

AU - Sanchez-Rodriguez, L. M.

AU - Brown, Marta

AU - Estévez, M.

AU - Machado, A.

AU - Jelinek, Herbert F.

PY - 2018

Y1 - 2018

N2 - Cardiac autonomic neuropathy (CAN) is a complication of diabetes with a long asymptomatic phase that is associated with high morbidity and mortality. Early identification of CAN in Type 1 diabetes mellitus (T1DM) may be possible using heart rate variability (HRV). However, the power of HRV analysis to identify CAN depends on the selection of suitable features that provide reliable information regarding cardiac autonomic regulation. Our aim was to compare the performance of Rényi entropy (RE) and permutation entropy (PE) for identification of T1DM patients with CAN. RE and PE measures from 235 data points and 5 min of cardiac interbeat interval (RR) sequences were analysed in 18 T1DM patients without CAN, 14 T1DM patients with CAN, and healthy controls matched for age and sex. RE was calculated for different orders α (-5, 5), pattern lengths λ (2, 4, 8), and tolerance σ. For PE analysis λ was set to (3-4) and time delays τ to (1-10). A forward stepwise discriminant analysis was carried out for estimating the classification functions. Accuracy was estimated following a K-fold cross-validation (k = 14). RE calculated for RR sequences of λ = 2, α > 0 showed the best performance for differentiating T1DM patients with CAN (p < 0.0001). PE measures showed better performance with ordinal patterns and τ = 4, 5 and 7 for differentiating patients with CAN. RE and PE provide complementary information achieving 100% classification accuracy (p < 0.0001 and p < 0.001, respectively). This approach might be promising as a sensitive and specific tool for CAN diagnosis in T1DM.

AB - Cardiac autonomic neuropathy (CAN) is a complication of diabetes with a long asymptomatic phase that is associated with high morbidity and mortality. Early identification of CAN in Type 1 diabetes mellitus (T1DM) may be possible using heart rate variability (HRV). However, the power of HRV analysis to identify CAN depends on the selection of suitable features that provide reliable information regarding cardiac autonomic regulation. Our aim was to compare the performance of Rényi entropy (RE) and permutation entropy (PE) for identification of T1DM patients with CAN. RE and PE measures from 235 data points and 5 min of cardiac interbeat interval (RR) sequences were analysed in 18 T1DM patients without CAN, 14 T1DM patients with CAN, and healthy controls matched for age and sex. RE was calculated for different orders α (-5, 5), pattern lengths λ (2, 4, 8), and tolerance σ. For PE analysis λ was set to (3-4) and time delays τ to (1-10). A forward stepwise discriminant analysis was carried out for estimating the classification functions. Accuracy was estimated following a K-fold cross-validation (k = 14). RE calculated for RR sequences of λ = 2, α > 0 showed the best performance for differentiating T1DM patients with CAN (p < 0.0001). PE measures showed better performance with ordinal patterns and τ = 4, 5 and 7 for differentiating patients with CAN. RE and PE provide complementary information achieving 100% classification accuracy (p < 0.0001 and p < 0.001, respectively). This approach might be promising as a sensitive and specific tool for CAN diagnosis in T1DM.

KW - Cardiac autonomic neuropathy

KW - Heart rate variability

KW - Ordinal patterns

KW - Permutation entropy

KW - Rényi entropy

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T3 - IFMBE Proceedings

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EP - 758

BT - EMBEC and NBC 2017

A2 - Eskola, Hannu

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PB - Springer-Verlag London Ltd.

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Carricarte-Naranjo C, Cornforth DJ, Sanchez-Rodriguez LM, Brown M, Estévez M, Machado A et al. Rényi and permutation entropy analysis for assessment of cardiac autonomic neuropathy. In Eskola H, Vaisanen O, Viik J, Hyttinen J, editors, EMBEC and NBC 2017: Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics. Vol. 65. Springer-Verlag London Ltd. 2018. p. 755-758. (IFMBE Proceedings). https://doi.org/10.1007/978-981-10-5122-7_189