Efficient Edge-AI Models for Robust ECG Abnormality Detection on Resource-Constrained Hardware Article Swipe
Zhaojing Huang
,
Luis Fernando Herbozo Contreras
,
Wing Leung
,
Leping Yu
,
Nhan Duy Truong
,
Armin Nikpour
,
Omid Kavehei
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.08.31.23294925
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.08.31.23294925
This study introduces two models, CLTC and CCfC, designed for abnormality identification using ECG data. Trained on the TNMG subset dataset, both models were evaluated for their performance, generative capacity, and resilience. They demonstrated comparable results in terms of F1 scores and AUROC values. The CCfC model achieved slightly higher accuracy, while the CLTC model showed better handling of empty channels. Remarkably, the models were successfully deployed on a resource-constrained microcontroller, proving their suitability for edge device applications. Generalization capabilities were confirmed through the evaluation of the CPSC dataset. The models’ efficient resource utilization, occupying 70.6% of total storage and 9.4% of flash memory, makes them promising candidates for real-world healthcare applications. Overall, this research advances abnormality identification in ECG data, contributing to the progress of AI in healthcare.
Related Topics
Concepts
Abnormality
Computer science
Generalization
Identification (biology)
Enhanced Data Rates for GSM Evolution
Resource (disambiguation)
Artificial intelligence
Machine learning
Pattern recognition (psychology)
Data mining
Medicine
Mathematics
Computer network
Psychiatry
Biology
Botany
Mathematical analysis
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.08.31.23294925
- https://www.medrxiv.org/content/medrxiv/early/2023/09/01/2023.08.31.23294925.full.pdf
- OA Status
- green
- Cited By
- 8
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386355230
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386355230Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.08.31.23294925Digital Object Identifier
- Title
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Efficient Edge-AI Models for Robust ECG Abnormality Detection on Resource-Constrained HardwareWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-09-01Full publication date if available
- Authors
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Zhaojing Huang, Luis Fernando Herbozo Contreras, Wing Leung, Leping Yu, Nhan Duy Truong, Armin Nikpour, Omid KaveheiList of authors in order
- Landing page
-
https://doi.org/10.1101/2023.08.31.23294925Publisher landing page
- PDF URL
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https://www.medrxiv.org/content/medrxiv/early/2023/09/01/2023.08.31.23294925.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://www.medrxiv.org/content/medrxiv/early/2023/09/01/2023.08.31.23294925.full.pdfDirect OA link when available
- Concepts
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Abnormality, Computer science, Generalization, Identification (biology), Enhanced Data Rates for GSM Evolution, Resource (disambiguation), Artificial intelligence, Machine learning, Pattern recognition (psychology), Data mining, Medicine, Mathematics, Computer network, Psychiatry, Biology, Botany, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 2, 2023: 2Per-year citation counts (last 5 years)
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23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| best_oa_location.license_id | https://openalex.org/licenses/other-oa |
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| best_oa_location.is_published | False |
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| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/other-oa |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
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| publication_year | 2023 |
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| abstract_inverted_index.AI | 127 |
| abstract_inverted_index.F1 | 40 |
| abstract_inverted_index.in | 37, 119, 128 |
| abstract_inverted_index.of | 39, 59, 86, 97, 102, 126 |
| abstract_inverted_index.on | 17, 68 |
| abstract_inverted_index.to | 123 |
| abstract_inverted_index.ECG | 14, 120 |
| abstract_inverted_index.The | 45, 90 |
| abstract_inverted_index.and | 7, 31, 42, 100 |
| abstract_inverted_index.for | 10, 26, 75, 109 |
| abstract_inverted_index.the | 18, 53, 63, 84, 87, 124 |
| abstract_inverted_index.two | 4 |
| abstract_inverted_index.9.4% | 101 |
| abstract_inverted_index.CCfC | 46 |
| abstract_inverted_index.CLTC | 6, 54 |
| abstract_inverted_index.CPSC | 88 |
| abstract_inverted_index.TNMG | 19 |
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| abstract_inverted_index.this | 114 |
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| abstract_inverted_index.CCfC, | 8 |
| abstract_inverted_index.data, | 121 |
| abstract_inverted_index.data. | 15 |
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| abstract_inverted_index.while | 52 |
| abstract_inverted_index.better | 57 |
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| abstract_inverted_index.subset | 20 |
| abstract_inverted_index.Trained | 16 |
| abstract_inverted_index.memory, | 104 |
| abstract_inverted_index.models, | 5 |
| abstract_inverted_index.proving | 72 |
| abstract_inverted_index.results | 36 |
| abstract_inverted_index.storage | 99 |
| abstract_inverted_index.through | 83 |
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| abstract_inverted_index.Abstract | 0 |
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| abstract_inverted_index.advances | 116 |
| abstract_inverted_index.dataset, | 21 |
| abstract_inverted_index.dataset. | 89 |
| abstract_inverted_index.deployed | 67 |
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| abstract_inverted_index.healthcare. | 129 |
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| abstract_inverted_index.microcontroller, | 71 |
| abstract_inverted_index.resource-constrained | 70 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
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| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I129604602 |
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| sustainable_development_goals[0].display_name | Decent work and economic growth |
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| citation_normalized_percentile.is_in_top_10_percent | True |