Evidence map›Paper›PMID 32103435›Full record

ArticleObesity surgery2020

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

Elisabeth R M Heremans, Amy S Chen, Ximeng Wang, Jiafei Cheng, Feng Xu, Agustin E Martinez, Georgios Lazaridis, Sabine Van Huffel, Jiande D Z Chen

Open access · greenAbstract read
PubMed Publisher
In one paragraph

Article in Obesity surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Transcutaneous Vagal Nerve Stimulation for Gastrointestinal Disorders.Journal of translational gastroenterology · 2025
    Article
  4. Article
  5. Noninvasive electrical neuromodulation for gastrointestinal motility disorders.Expert review of gastroenterology & hepatology · 2023
    Review
  6. Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 3 countries.

Elisabeth R M HeremansDepartments of Medicine and Biomedical Engineering, Johns Hopkins University, School of Medicine, Baltimore, MD, USA.
Amy S ChenCollege of Letters and Science, University of California, Berkeley, CA, USA.
Ximeng WangDepartments of Medicine and Biomedical Engineering, Johns Hopkins University, School of Medicine, Baltimore, MD, USA.
Jiafei ChengDepartments of Medicine and Biomedical Engineering, Johns Hopkins University, School of Medicine, Baltimore, MD, USA.
Feng XuYinzhou Hospital Affiliated with Ningbo University School of Medicine, Ningbo, China.
Agustin E MartinezDepartment of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, B-3001, Leuven, Belgium.
Georgios LazaridisDepartment of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, B-3001, Leuven, Belgium.
Sabine Van HuffelDepartment of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, B-3001, Leuven, Belgium.
Jiande D Z ChenDepartments of Medicine and Biomedical Engineering, Johns Hopkins University, School of Medicine, Baltimore, MD, USA. jchen184@jhu.edu.ORCID http://orcid.org/0000-0002-0191-5697
Johns Hopkins University · USKU Leuven · BEBerkeley College · USNingbo University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeNeuromodulation, 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

methodsTwo 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.

resultsThe 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.

conclusionsAutomatic 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.

Indexed as

Diabetes MellitusObesity, MorbidEatingHumansNeural Networks, ComputerObesityDiabetesFood intake detectionMachine learningNeuromodulationObesity

Identifiers

PMID32103435
OpenAlexW3007223654

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.