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

• Published in: IEEE transaction on neural networks and learning systems Vol. 34; no. 10; pp. 6983 - 7003
• Main Authors: Mao, Shitong, Sejdic, Ervin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anomalies; Artificial Intelligence; Biomedical monitoring; Brain modeling; Computer architecture; Computer networks; Computer vision; Decision making; Deep learning; human physiology; Humans; Logic gates; Machine Learning; Microprocessors; Morbidity; Natural Language Processing; Neural networks; Neural Networks, Computer; Physiology; recurrent neural network (RNN); Recurrent neural networks; signal processing; Speech recognition
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2022.3145365

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.