TinyML and edge intelligence applications in cardiovascular disease: A survey

Tiny machine learning (TinyML) and edge intelligence have emerged as pivotal paradigms for enabling machine learning on resource-constrained devices situated at the extreme edge of networks. In this paper, we explore the transformative potential of TinyML in facilitating pervasive, low-power cardiov...

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Veröffentlicht in:Computers in biology and medicine Jg. 186; S. 109653
Hauptverfasser: Keivanimehr, Ali Reza, Akbari, Mohammad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Ltd 01.03.2025
Elsevier Limited
Schlagworte:
Algorithms
Artificial intelligence
Artificial neural networks
Belief networks
Cardiovascular disease
Cardiovascular diseases
Cardiovascular Diseases - diagnosis
Cardiovascular Diseases - physiopathology
Cloud computing
Constraints
Deep learning
Deep neural networks
Edge intelligence
EKG
Electrocardiography
Energy consumption
Health care industry
Heart
Humans
Intelligence
Internal Medicine
Internet of Things
Learning algorithms
Machine Learning
Monitoring
Neural networks
Neural Networks, Computer
Other
Power management
Privacy
Real time
Software
Telemedicine
TinyML
Wearable computers
Wearable Electronic Devices
Wearable technology
Wide area networks
Internet of things
Deep neural networks
Cardiovascular diseases
TinyML
Edge intelligence
ISSN:0010-4825, 1879-0534, 1879-0534
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Zusammenfassung:Tiny machine learning (TinyML) and edge intelligence have emerged as pivotal paradigms for enabling machine learning on resource-constrained devices situated at the extreme edge of networks. In this paper, we explore the transformative potential of TinyML in facilitating pervasive, low-power cardiovascular monitoring and real-time analytics for patients with cardiac anomalies, leveraging wearable devices as the primary interface. To begin with, we provide an overview of TinyML software and hardware enablers, accompanied by an examination of networking solutions such as Low-power Wide area network (LPWAN) that facilitate the seamless deployment of TinyML frameworks. Following this, we delve into the methodologies of knowledge distillation, quantization, and pruning, which represent the cornerstone strategies for optimizing machine learning models to operate efficiently within resource-constrained environments. Furthermore, our discussion extends to the role of efficient deep neural networks tailored specifically for cardiovascular monitoring on wearable devices with limited computational resources. Through a comprehensive review, we analyze the applications of prominent artificial neural network architectures including Convolutional Neural Networks (CNNs), Autoencoders, Deep Belief Networks (DBNs), and Transformers in the domain of Electrocardiogram (ECG) analytics, shedding light on their efficacy and potential in advancing healthcare technology. [Display omitted] •TinyML combined with Internet of Medical Things (IoMT) offers great potential for cardiovascular disease monitoring.•A comprehensive review of TinyML enablers such as software, hardware, and communication technologies is conducted.•Well-known compression techniques like Knowledge distillation, quantization, and Neural Architecture Search are introduced.•Deep learning models that are highly used in arrhythmia detection task are analyzed and compared using various metrics.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2025.109653