Mathematical and informational tools for classifying blood glucose signals - a pilot study
A survey campaign was carried out on the dynamics of blood glucose measured through interstitial sensors of relative recent diffusion in the market. These sensors generated time series that were labeled according to medical diagnosis in diabetics and non-diabetics, and that constituted the data core...
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Published in | Physica A Vol. 626; p. 129071 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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Elsevier B.V
15.09.2023
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Abstract | A survey campaign was carried out on the dynamics of blood glucose measured through interstitial sensors of relative recent diffusion in the market. These sensors generated time series that were labeled according to medical diagnosis in diabetics and non-diabetics, and that constituted the data core of the classification models. Based on the calculation of the distribution of ordinal patterns of the time series, the corresponding points in the entropy-complexity causal plane were located. Moreover, the transition matrices of these ordinal patterns (OPTMs) were calculated in order to find the proximity using the Manhattan distance of every OPTM with respect to the mean of each group, associating the corresponding signal to each class. On the other hand, the Frobenius norm of every OPTM and the norm of its stationary vector were computed given different values for the considered classes. The effect of repeated values in a signal was also analyzed. Notable differences were obtained in the properties of the OPTMs of each class. In another sense, it is shown that diabetes is a disease that reduces the entropy of the temporal evolution of blood glucose in well-defined time periods and presents values of complexity significantly higher than those obtained in subjects without diabetes. The selected alternatives coincide in detecting patients positively diagnosed with Type II Diabetes Mellitus. The calculations on the OPTMs show the correlation among patterns of the signals. At the same time, in the entropy-complexity plane, the considered groups were located in well-defined regions showing the differentiating power of these information measures and indicating variations in the dynamics of the biological system when diabetes is present. With the four mathematical tools selected and the dynamical characterization given by the causal plane, it was possible to define an index that clearly differentiates the classes under study.
•A simple and effective index to detect DMT2 is proposed and validated.•Matrix measures computed to the signal itself and to compare it with reference ones.•A novel application of the entropy-complexity plane is presented.•Tied data analysis is incorporated the treatment of ordinal patterns.•Macroscopic properties help to understand both, microscopic and whole system. |
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AbstractList | A survey campaign was carried out on the dynamics of blood glucose measured through interstitial sensors of relative recent diffusion in the market. These sensors generated time series that were labeled according to medical diagnosis in diabetics and non-diabetics, and that constituted the data core of the classification models. Based on the calculation of the distribution of ordinal patterns of the time series, the corresponding points in the entropy-complexity causal plane were located. Moreover, the transition matrices of these ordinal patterns (OPTMs) were calculated in order to find the proximity using the Manhattan distance of every OPTM with respect to the mean of each group, associating the corresponding signal to each class. On the other hand, the Frobenius norm of every OPTM and the norm of its stationary vector were computed given different values for the considered classes. The effect of repeated values in a signal was also analyzed. Notable differences were obtained in the properties of the OPTMs of each class. In another sense, it is shown that diabetes is a disease that reduces the entropy of the temporal evolution of blood glucose in well-defined time periods and presents values of complexity significantly higher than those obtained in subjects without diabetes. The selected alternatives coincide in detecting patients positively diagnosed with Type II Diabetes Mellitus. The calculations on the OPTMs show the correlation among patterns of the signals. At the same time, in the entropy-complexity plane, the considered groups were located in well-defined regions showing the differentiating power of these information measures and indicating variations in the dynamics of the biological system when diabetes is present. With the four mathematical tools selected and the dynamical characterization given by the causal plane, it was possible to define an index that clearly differentiates the classes under study.
•A simple and effective index to detect DMT2 is proposed and validated.•Matrix measures computed to the signal itself and to compare it with reference ones.•A novel application of the entropy-complexity plane is presented.•Tied data analysis is incorporated the treatment of ordinal patterns.•Macroscopic properties help to understand both, microscopic and whole system. |
ArticleNumber | 129071 |
Author | Otero, Dino Blesa, Manuel García Amadio, Ariel Legnani, Walter Rey, Andrea Bonini, Cristian |
Author_xml | – sequence: 1 givenname: Ariel surname: Amadio fullname: Amadio, Ariel organization: Vehicle Research, Development, and Innovation Center, Universidad Tecnológica Nacional Facultad Regional General Pacheco, Av. Hipólito Yrigoyen 288, B1617, General Pacheco, Argentina – sequence: 2 givenname: Andrea orcidid: 0000-0002-9185-1382 surname: Rey fullname: Rey, Andrea email: arey@frba.utn.edu.ar organization: Signal and Image Processing Center, Universidad Tecnológica Nacional Facultad Regional Buenos Aires, Av. Medrano 951, C1179, Buenos Aires, Argentina – sequence: 3 givenname: Walter orcidid: 0000-0002-6949-0728 surname: Legnani fullname: Legnani, Walter organization: Signal and Image Processing Center, Universidad Tecnológica Nacional Facultad Regional Buenos Aires, Av. Medrano 951, C1179, Buenos Aires, Argentina – sequence: 4 givenname: Manuel García surname: Blesa fullname: Blesa, Manuel García organization: Signal and Image Processing Center, Universidad Tecnológica Nacional Facultad Regional Buenos Aires, Av. Medrano 951, C1179, Buenos Aires, Argentina – sequence: 5 givenname: Cristian orcidid: 0000-0001-8421-1339 surname: Bonini fullname: Bonini, Cristian organization: Research, Development, and Innovation in Electric Energy Center, Universidad Tecnológica Nacional Facultad Regional General Pacheco, Av. Hipólito Yrigoyen 288, B1617, General Pacheco, Argentina – sequence: 6 givenname: Dino surname: Otero fullname: Otero, Dino organization: Vehicle Research, Development, and Innovation Center, Universidad Tecnológica Nacional Facultad Regional General Pacheco, Av. Hipólito Yrigoyen 288, B1617, General Pacheco, Argentina |
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Cites_doi | 10.1109/TETCI.2018.2866254 10.1056/NEJM197604292941811 10.3390/rs11020196 10.3390/e14081553 10.1089/dia.2018.0092 10.1016/j.physleta.2017.03.052 10.1007/s11517-020-02132-w 10.3343/alm.2013.33.6.393 10.1016/0375-9601(95)00867-5 10.1016/j.procs.2019.08.140 10.1126/science.401543 10.1016/j.chaos.2021.110798 10.1140/epjb/e2013-30764-5 10.1016/j.amc.2019.06.068 10.1103/PhysRevE.100.042304 10.1209/0295-5075/ac6a72 10.1159/000089312 10.1016/j.imu.2019.100179 10.1016/j.physa.2005.11.053 10.1063/1.4917289 10.1088/0031-9155/9/2/307 10.1103/PhysRevE.85.021906 10.1103/PhysRevLett.88.174102 10.1089/dia.2011.0099 10.1016/j.ab.2010.12.019 10.1007/s11071-021-07059-x 10.1063/1.4919075 10.1063/1.5142500 10.1098/rsta.2014.0091 10.1161/01.CIR.101.23.e215 10.19139/soic-2310-5070-1523 |
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