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

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.

Fig. 1.

Computational graph of RNN. $o$ is the RNN output, and $L$ presents the difference between the RNN output and the desired output (target or label). $L$ is commonly used for calculating the loss function.

Fig. 2.

LSTM recurrent neural network. The computational graph is shown in (a). The LSTM has an extra pathway for the cell state. A recurrent unit of LSTM is shown in (b). The arrows in blue represent the internal cell state.

Fig. 3.

The unit structure of GRU.

Fig. 4.

The general bidirectional RNN has two time flow paths. The variables ${\mathit{h}}^{\left(t\right)}$ and ${\mathit{g}}^{\left(t\right)}$ present the hidden state for the sub-RNN moving forward and backward, respectively.

Fig. 5.

The implementations of RNN models are determined by the label structure of each signal sample. (a) shows a signal sequence with a sequential label. The general applied RNN could be designed in (c). Sometimes a signal sequence could only have one annotated label, as shown in (b), and the RNN could be designed in the form of (d). Although (c) and (d) show one-layer unidirectional RNN, multiple stacked layers or bidirectional RNN are also adoptable.

Fig. 6.

The experiment designs in computational physiology. (a)Cross-subject prediction; (b), (c), and (d) describe three different strategies of within-subject prediction. (b): the mixed manner; (c) patient-specific manner; (d) fine-tuning manner

Fig. 7.

Representative applications of RNN in the human body for diagnosis and event detection.