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Artificial Neural Network-Based Automatic Detection of Food Intake for Neuromodulation in Treating Obesity and Diabetes

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Abstract

Purpose

Neuromodulation, such as vagal nerve stimulation and intestinal electrical stimulation, has been introduced for the treatment of obesity and diabetes. Ideally, neuromodulation should be applied automatically after food intake. The purpose of this study was to develop a method of automatic food intake detection through dynamic analysis of heart rate variability (HRV).

Materials and Methods

Two experiments were conducted: (1) a small sample series with a standard test meal and (2) a large sample series with varying meal size. Electrocardiograms (ECGs) were collected in the fasting and postprandial states. Each ECG was processed to compute the HRV. For each HRV segment, time- and frequency-domain features were derived and used as inputs to train and test an artificial neural network (ANN). The ANN was trained and tested with different cross-validation methods.

Results

The highest classification accuracy reached with leave-one-subject-out-leave-one-sample-out cross-validation was 0.93 in experiment 1 and 0.88 in experiment 2. Retraining the ANN on recordings of a subject drastically increased the achieved accuracy for that subject to values of 0.995 and 0.95 in experiments 1 and 2, respectively.

Conclusions

Automatic food intake detection by ANNs, using features from the HRV, is feasible and may have a great potential for neuromodulation-based treatments of meal-related disorders.

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Abbreviations

ANN:

Artificial neural networks

FGIDs:

Functional gastrointestinal disorders

HRV:

Heart rate variability

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Authors and Affiliations

  1. Departments of Medicine and Biomedical Engineering, Johns Hopkins University, School of Medicine, Baltimore, MD, USA

    Elisabeth R. M. Heremans, Ximeng Wang, Jiafei Cheng & Jiande D. Z. Chen

  2. Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, B-3001, Leuven, Belgium

    Elisabeth R. M. Heremans, Agustin E. Martinez, Georgios Lazaridis & Sabine Van Huffel

  3. College of Letters and Science, University of California, Berkeley, CA, USA

    Amy S. Chen

  4. Yinzhou Hospital Affiliated with Ningbo University School of Medicine, Ningbo, China

    Feng Xu

Authors
  1. Elisabeth R. M. Heremans
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  2. Amy S. Chen
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  3. Ximeng Wang
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  4. Jiafei Cheng
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  5. Feng Xu
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  9. Jiande D. Z. Chen
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Correspondence to Jiande D. Z. Chen.

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Ethical Approval Statement

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Heremans, E.R.M., Chen, A.S., Wang, X. et al. Artificial Neural Network-Based Automatic Detection of Food Intake for Neuromodulation in Treating Obesity and Diabetes. OBES SURG 30, 2547–2557 (2020). https://doi.org/10.1007/s11695-020-04511-6

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  • DOI: https://doi.org/10.1007/s11695-020-04511-6

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