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Chai L, Xu H, Luo Z, Li S. A multi-source heterogeneous data analytic method for future price fluctuation prediction. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.07.073] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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2
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Prediction of blood glucose concentration for type 1 diabetes based on echo state networks embedded with incremental learning. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.10.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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3
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Juan CG, García H, Ávila-Navarro E, Bronchalo E, Galiano V, Moreno Ó, Orozco D, Sabater-Navarro JM. Feasibility study of portable microwave microstrip open-loop resonator for non-invasive blood glucose level sensing: proof of concept. Med Biol Eng Comput 2019; 57:2389-2405. [PMID: 31473945 DOI: 10.1007/s11517-019-02030-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 08/10/2019] [Indexed: 01/05/2023]
Abstract
Self-management of blood glucose level is part and parcel of diabetes treatment, which involves invasive, painful, and uncomfortable methods. A proper non-invasive blood glucose monitor (NIBGM) is therefore desirable to deal better with it. Microwave resonators can potentially be used for such a purpose. Following the positive results from an in vitro previous work, a portable device based upon a microwave resonator was developed and assessed in a multicenter proof of concept. Its electrical response was analyzed when an individual's tongue was placed onto it. The study was performed with 352 individuals during their oral glucose tolerance tests, having four measurements per individual. The findings revealed that the accuracy must be improved before the diabetes community can make real use of the device. However, the relationship between the measuring parameter and the individual's blood glucose level is coherent with that from previous works, although with higher data dispersion. This is reflected in correlation coefficients between glycemia and the measuring magnitude consistently negative, although small, for the different datasets analyzed. Further research is proposed, focused on system improvements, individual calibration, and multitechnology approach. The study of the influence of other blood components different to glucose is also advised. Graphical abstract.
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Affiliation(s)
- Carlos G Juan
- Department of Systems Engineering and Automation, Miguel Hernández University, Elche, Spain
| | - Héctor García
- Department of Materials Science, Optics and Electronic Technology, Miguel Hernández University, Elche, Spain
| | - Ernesto Ávila-Navarro
- Department of Materials Science, Optics and Electronic Technology, Miguel Hernández University, Elche, Spain
| | - Enrique Bronchalo
- Department of Communications Engineering, Miguel Hernández University, Elche, Spain
| | - Vicente Galiano
- Department of Computer Engineering, Miguel Hernández University, Elche, Spain
| | - Óscar Moreno
- Department of Clinical Medicine, Miguel Hernández University, Elche, Spain
| | - Domingo Orozco
- Department of Clinical Medicine, Miguel Hernández University, Elche, Spain
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Glucose Concentration Measurement in Human Blood Plasma Solutions with Microwave Sensors. SENSORS 2019; 19:s19173779. [PMID: 31480415 PMCID: PMC6749577 DOI: 10.3390/s19173779] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 08/27/2019] [Accepted: 08/28/2019] [Indexed: 12/29/2022]
Abstract
Three microwave sensors are used to track the glucose level of different human blood plasma solutions. In this paper, the sensors are evaluated as glucose trackers in a context close to real human blood. Different plasma solutions sets were prepared from a human blood sample at several added glucose concentrations up to 10 wt%, adding also ascorbic acid and lactic acid at different concentrations. The experimental results for the different sensors/solutions combinations are presented in this work. The sensors show good performance and linearity as glucose level retrievers, although the sensitivities change as the rest of components vary. Different sensor behaviors depending upon the concentrations of glucose and other components are identified and characterized. The results obtained in terms of sensitivity are coherent with previous works, highlighting the contribution of glucose to the dielectric losses of the solution. The results are also consistent with the frequency evolution of the electromagnetic signature of glucose found in the literature, and are helpful for selecting frequency bands for sensing purposes and envisioning future approaches to the challenging measurement in real biological contexts. Discussion of the implications of the results and guidelines for further research and development of more accurate sensors is offered.
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Short-term prediction of glucose in type 1 diabetes using kernel adaptive filters. Med Biol Eng Comput 2018; 57:27-46. [PMID: 29967934 DOI: 10.1007/s11517-018-1859-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 06/11/2018] [Indexed: 10/28/2022]
Abstract
This study aims at presenting a nonlinear, recursive, multivariate prediction model of the subcutaneous glucose concentration in type 1 diabetes. Nonlinear regression is performed in a reproducing kernel Hilbert space, by either the fixed budget quantized kernel least mean square (QKLMS-FB) or the approximate linear dependency kernel recursive least-squares (KRLS-ALD) algorithm, such that a sparse model structure is accomplished. A multivariate feature set (i.e., subcutaneous glucose, food carbohydrates, insulin regime and physical activity) is used and its influence on short-term glucose prediction is investigated. The method is evaluated using data from 15 patients with type 1 diabetes in free-living conditions. In the case when all the input variables are considered: (i) the average root mean squared error (RMSE) of QKLMS-FB increases from 13.1 mg dL-1 (mean absolute percentage error (MAPE) 6.6%) for a 15-min prediction horizon (PH) to 37.7 mg dL-1 (MAPE 20.8%) for a 60-min PH and (ii) the RMSE of KRLS-ALD, being predictably lower, increases from 10.5 mg dL-1 (MAPE 5.2%) for a 15-min PH to 31.8 mg dL-1 (MAPE 18.0%) for a 60-min PH. Multivariate data improve systematically both the regularity and the time lag of the predictions, reducing the errors in critical glucose value regions for a PH ≥ 30 min. Graphical abstract ᅟ.
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Yu X, Turksoy K, Rashid M, Feng J, Frantz N, Hajizadeh I, Samadi S, Sevil M, Lazaro C, Maloney Z, Littlejohn E, Quinn L, Cinar A. Model-Fusion-Based Online Glucose Concentration Predictions in People with Type 1 Diabetes. CONTROL ENGINEERING PRACTICE 2018; 71:129-141. [PMID: 29276347 PMCID: PMC5736323 DOI: 10.1016/j.conengprac.2017.10.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Accurate predictions of glucose concentrations are necessary to develop an artificial pancreas (AP) system for people with type 1 diabetes (T1D). In this work, a novel glucose forecasting paradigm based on a model fusion strategy is developed to accurately characterize the variability and transient dynamics of glycemic measurements. To this end, four different adaptive filters and a fusion mechanism are proposed for use in the online prediction of future glucose trajectories. The filter fusion mechanism is developed based on various prediction performance indexes to guide the overall output of the forecasting paradigm. The efficiency of the proposed model fusion based forecasting method is evaluated using simulated and clinical datasets, and the results demonstrate the capability and prediction accuracy of the data-based fusion filters, especially in the case of limited data availability. The model fusion framework may be used in the development of an AP system for glucose regulation in patients with T1D.
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Affiliation(s)
- Xia Yu
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, PR China
| | - Kamuran Turksoy
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Jianyuan Feng
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Nicole Frantz
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Iman Hajizadeh
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Sediqeh Samadi
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Mert Sevil
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Caterina Lazaro
- Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Zacharie Maloney
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Elizabeth Littlejohn
- Department of Pediatrics and Medicine, Kovler Diabetes Center, University of Chicago, Chicago, IL 60637, USA
| | - Laurie Quinn
- Department of Biobehavioral Health Science, College of Nursing, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Ali Cinar
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
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7
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Zhao H, Zhao C, Yu C, Dassau E. Multiple order model migration and optimal model selection for online glucose prediction in Type 1 diabetes. AIChE J 2017. [DOI: 10.1002/aic.15983] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Hong Zhao
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering; Zhejiang University; Hangzhou 310027 China
| | - Chunhui Zhao
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering; Zhejiang University; Hangzhou 310027 China
| | - Chengxia Yu
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering; Zhejiang University; Hangzhou 310027 China
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences; Harvard University; Cambridge MA 02138
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Oviedo S, Vehí J, Calm R, Armengol J. A review of personalized blood glucose prediction strategies for T1DM patients. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2017; 33:e2833. [PMID: 27644067 DOI: 10.1002/cnm.2833] [Citation(s) in RCA: 107] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 09/15/2016] [Accepted: 09/16/2016] [Indexed: 06/06/2023]
Abstract
This paper presents a methodological review of models for predicting blood glucose (BG) concentration, risks and BG events. The surveyed models are classified into three categories, and they are presented in summary tables containing the most relevant data regarding the experimental setup for fitting and testing each model as well as the input signals and the performance metrics. Each category exhibits trends that are presented and discussed. This document aims to be a compact guide to determine the modeling options that are currently being exploited for personalized BG prediction.
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Affiliation(s)
- Silvia Oviedo
- Institut d'Informàtica i Aplicacions, Parc Científic i Tecnològic de la Universitat de Girona, 17003, Girona, Spain
| | - Josep Vehí
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, Edifici P4, 17071, Girona, Spain
| | - Remei Calm
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, Edifici P4, 17071, Girona, Spain
| | - Joaquim Armengol
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, Edifici P4, 17071, Girona, Spain
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Dielectric characterization of water glucose solutions using a transmission/reflection line method. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.07.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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10
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Beštek M, Kocev D, Džeroski S, Brodnik A, Iljaž R. Modelling Time-Series of Glucose Measurements from Diabetes Patients Using Predictive Clustering Trees. Artif Intell Med 2017. [DOI: 10.1007/978-3-319-59758-4_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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11
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Continuous Glucose Monitoring Sensors: Past, Present and Future Algorithmic Challenges. SENSORS 2016; 16:s16122093. [PMID: 27941663 PMCID: PMC5191073 DOI: 10.3390/s16122093] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Revised: 11/17/2016] [Accepted: 12/07/2016] [Indexed: 11/18/2022]
Abstract
Continuous glucose monitoring (CGM) sensors are portable devices that allow measuring and visualizing the glucose concentration in real time almost continuously for several days and are provided with hypo/hyperglycemic alerts and glucose trend information. CGM sensors have revolutionized Type 1 diabetes (T1D) management, improving glucose control when used adjunctively to self-monitoring blood glucose systems. Furthermore, CGM devices have stimulated the development of applications that were impossible to create without a continuous-time glucose signal, e.g., real-time predictive alerts of hypo/hyperglycemic episodes based on the prediction of future glucose concentration, automatic basal insulin attenuation methods for hypoglycemia prevention, and the artificial pancreas. However, CGM sensors’ lack of accuracy and reliability limited their usability in the clinical practice, calling upon the academic community for the development of suitable signal processing methods to improve CGM performance. The aim of this paper is to review the past and present algorithmic challenges of CGM sensors, to show how they have been tackled by our research group, and to identify the possible future ones.
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