Key result
A gated recurrent unit (GRU) model with three layers achieved the best performance among tested deep learning architectures for predicting heart rate 5 minutes in advance.
Why the study?
Low heart rate increases the risk of death and cardiovascular diseases, making real-time monitoring and early prediction of irregularity critical.
A real-time prediction system using a 3-layer GRU model and stream processing platforms can effectively predict heart rate 5 minutes in advance.
Low heart rate causes a risk of death, heart disease, and cardiovascular diseases. Therefore, monitoring the heart rate is critical because of the heart’s function to discover its irregularity to detect the health problems early. Rapid technological advancement (e.g., artificial intelligence and stream processing technologies) allows healthcare sectors to consolidate and analyze massive health‐based data to discover risks by making more accurate predictions. Therefore, this work proposes a real‐time prediction system for heart rate, which helps the medical care providers and patients avoid heart rate risk in real time. The proposed system consists of two phases, namely, an offline phase and an online phase. The offline phase targets developing the model using different forecasting techniques to find the lowest root mean square error. The heart rate time‐series dataset is extracted from Medical Information Mart for Intensive Care (MIMIC‐II). Recurrent neural network (RNN), long short‐term memory (LSTM), gated recurrent units (GRU), and bidirectional long short‐term memory (BI‐LSTM) are applied to heart rate time series. For the online phase, Apache Kafka and Apache Spark have been used to predict the heart rate in advance based on the best developed model. According to the experimental results, the GRU with three layers has recorded the best performance. Consequently, GRU with three layers has been used to predict heart rate 5 minutes in advance.
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Alharbi et al. (2021) studied Heart rate prediction. Gated recurrent units (GRU) with three layers vs. RNN, LSTM, and BI-LSTM models was evaluated on Root mean square error for heart rate prediction. A gated recurrent unit (GRU) model with three layers achieved the best performance among tested deep learning architectures for predicting heart rate 5 minutes in advance.
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