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Cited by 128Machine Learning in HealthcareECG Monitoring and AnalysisNeural Networks and Applications

A Review of Recurrent Neural Network-Based Methods in Computational Physiology

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Mao, Shitong, Sejdić, Ervin·2023·IEEE Transactions on Neural Networks and Learning Systems
DOI 10.1109/tnnls.2022.3145365PMC PMC10589904
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Abstract

Artificial intelligence and machine learning techniques have progressed dramatically and become powerful tools required to solve complicated tasks, such as computer vision, speech recognition, and natural language processing. Since these techniques have provided promising and evident results in these fields, they emerged as valuable methods for applications in human physiology and healthcare. General physiological recordings are time-related expressions of bodily processes associated with health or morbidity. Sequence classification, anomaly detection, decision making, and future status prediction drive the learning algorithms to focus on the temporal pattern and model the nonstationary dynamics of the human body. These practical requirements give birth to the use of recurrent neural networks (RNNs), which offer a tractable solution in dealing with physiological time series and provide a way to understand complex time variations and dependencies. The primary objective of this article is to provide an overview of current applications of RNNs in the area of human physiology for automated prediction and diagnosis within different fields. Finally, we highlight some pathways of future RNN developments for human physiology.

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Cited by

Sorted by citations

Authors
Mao, S., & Sejdić, E.
Affiliations
  • University of Pittsburgh (US)
  • University of Toronto (CA)
ORCID
Venue
IEEE Transactions on Neural Networks and Learning Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2023-10
Volume
34
Issue
10
Pages
6983-7003
ISSN
  • 2162-237X (ISSN-L)
  • 2162-2388
DOI
10.1109/tnnls.2022.3145365
PMC
PMC10589904
OpenAlex
W4210847455
Cited by
128
References
5
Citations by year
2026: 41 · 2025: 37 · 2024: 29 · 2023: 18 · 2022: 3
FWCI
1.52
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion, other-oa)
Funders
Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health (R01HD092239, R01HD074819)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords
recurrent neural networks, computational physiology, physiological time series, physiological signal classification, anomaly detection, automated diagnosis, temporal dependencies, non-stationary dynamics
MeSH terms
Machine Learning, Algorithms, Artificial Intelligence, Humans, Natural Language Processing, Neural Networks, Computer

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