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Hardware real-time individualised blood glucose predictor generator based on grammars and cartesian genetic programming

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Abstract

In this paper, we introduce a novel grammar-guided technique based on genetic programming for on-chip, real-time, configurable hardware design of model generators on an FPGA. The technique integrates grammar-based design, Cartesian Genetic Programming, and a (1+\(\lambda\)) Evolutionary Strategy and is demonstrated through the implementation of a wearable hardware predictor for blood glucose prediction. People with diabetes need to manage their blood glucose levels to prevent life-threatening situations and long-term complications. Effective glucose management requires accurate blood glucose predictions, yet most existing methods rely on heuristic estimators. This system enables the training and testing of personalized models using real patient data. We validated the approach by generating and evaluating models for 30- and 60-min forecasting predictions on ten patients, creating a total of 200 models. The system achieved state-of-the-art results, with 98% and 90% of predictions falling within clinically acceptable regions according to Clarke error grid analysis, for 30- and 60-min horizons, respectively. Unlike software implementations, our technique does not suffer from hardware limitations and provides an efficient, adaptable solution through wearable hardware with minimal errors and low power consumption. This is the first demonstration of combining Cartesian Genetic Programming with a hardware implementation for grammar-based blood glucose prediction, potentially enabling real-time embedded systems for portable devices.

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Data availibility

Data set is available at HUPA-UCM Diabetes Dataset [29].

Notes

  1. https://www.freestyle.abbott/uk-en/home.html.

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Funding

This work is supported by the Spanish Ministerio de Innovación Ciencia y Universidad - grants PID2021-125549OB-I00 and PDC2022-133429-I00/AEI/10.13039/501100011033 European Union Resilience and Recovery Mechanism, Funded by the European Union - NextGenerationEU.

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Author notes

  1. J. Ignacio Hidalgo and Óscar Garnica contributed equally to this work.

Authors and Affiliations

  1. Departamento de Arquitectura de Computadores y Automática, Universidad Complutense de Madrid, Calle del Prof. José García Santesmases, 28040, Madrid, Comunidad de Madrid, Spain

    Jorge Cano, J. Ignacio Hidalgo & Óscar Garnica

Authors
  1. Jorge Cano
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  2. J. Ignacio Hidalgo
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  3. Óscar Garnica
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Contributions

JC wrote the main manuscript. JIH and OG critically reviewed the manuscript and made any necessary changes JC prepared the figures. JIH and OG reviewed the figures. JIH and OG reviewed the research process.

Corresponding authors

Correspondence to Jorge Cano or J. Ignacio Hidalgo.

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Conflict of interest

The authors declare no competing interests.

Ethics approval

This study is in accordance with the ethical standards of the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Data have been specifically collected for this study. They are included in a database created for the project F11/2018, approved by the Hospital Universitario Principe de Asturias’s Clinical Research Ethics Committee. Participants signed an informed consent form.

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Cano, J., Hidalgo, J.I. & Garnica, Ó. Hardware real-time individualised blood glucose predictor generator based on grammars and cartesian genetic programming. Genet Program Evolvable Mach 26, 3 (2025). https://doi.org/10.1007/s10710-024-09500-7

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