Advancements in Continuous Glucose Monitoring: Integrating Deep Learning and ECG Signal Article Swipe
MohammadReza Hosseinzadehketilateh
,
Banafsheh Adami
,
Nima Karimian
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.07296
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.07296
This paper presents a novel approach to noninvasive hyperglycemia monitoring utilizing electrocardiograms (ECG) from an extensive database comprising 1119 subjects. Previous research on hyperglycemia or glucose detection using ECG has been constrained by challenges related to generalization and scalability, primarily due to using all subjects' ECG in training without considering unseen subjects as a critical factor for developing methods with effective generalization. We designed a deep neural network model capable of identifying significant features across various spatial locations and examining the interdependencies among different features within each convolutional layer. To expedite processing speed, we segment the ECG of each user to isolate one heartbeat or one cycle of the ECG. Our model was trained using data from 727 subjects, while 168 were used for validation. The testing phase involved 224 unseen subjects, with a dataset consisting of 9,000 segments. The result indicates that the proposed algorithm effectively detects hyperglycemia with a 91.60% area under the curve (AUC), 81.05% sensitivity, and 85.54% specificity.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2403.07296
- https://arxiv.org/pdf/2403.07296
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392781909
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392781909Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2403.07296Digital Object Identifier
- Title
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Advancements in Continuous Glucose Monitoring: Integrating Deep Learning and ECG SignalWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-03-12Full publication date if available
- Authors
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MohammadReza Hosseinzadehketilateh, Banafsheh Adami, Nima KarimianList of authors in order
- Landing page
-
https://arxiv.org/abs/2403.07296Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2403.07296Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2403.07296Direct OA link when available
- Concepts
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Continuous glucose monitoring, Deep learning, SIGNAL (programming language), Computer science, Artificial intelligence, Machine learning, Medicine, Internal medicine, Glycemic, Insulin, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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