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?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s12265-024-10504-y
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s12265-024-10504-y
This study introduces two models, ConvLSTM2D-liquid time-constant network (CLTC) and ConvLSTM2D-closed-form continuous-time neural network (CCfC), designed for abnormality identification using electrocardiogram (ECG) data. Trained on the Telehealth Network of Minas Gerais (TNMG) subset dataset, both models were evaluated for their performance, generalizability 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 on the China Physiological Signal Challenge 2018 (CPSC) dataset. The models’ efficient resource utilization, occupying 70.6% of memory 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. Graphical Abstract
Related Topics
Concepts
Computer science
Abnormality
Generalizability theory
Artificial intelligence
Generalization
Identification (biology)
Artificial neural network
Machine learning
Resource (disambiguation)
Data mining
Pattern recognition (psychology)
Medicine
Mathematical analysis
Psychiatry
Biology
Botany
Mathematics
Statistics
Computer network
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s12265-024-10504-y
- OA Status
- hybrid
- Cited By
- 13
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394738178
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394738178Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s12265-024-10504-yDigital Object Identifier
- Title
-
Efficient Edge-AI Models for Robust ECG Abnormality Detection on Resource-Constrained HardwareWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-12Full publication date if available
- Authors
-
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/10.1007/s12265-024-10504-yPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1007/s12265-024-10504-yDirect OA link when available
- Concepts
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Computer science, Abnormality, Generalizability theory, Artificial intelligence, Generalization, Identification (biology), Artificial neural network, Machine learning, Resource (disambiguation), Data mining, Pattern recognition (psychology), Medicine, Mathematical analysis, Psychiatry, Biology, Botany, Mathematics, Statistics, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 10, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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24Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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