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.

中文翻译：

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使用深度学习和流处理平台的心率实时系统预测

低心率会导致死亡，心脏病和心血管疾病的风险。因此，由于心脏具有发现心律不齐以及早发现健康问题的功能，因此监测心率至关重要。快速的技术进步（例如，人工智能和流处理技术）使医疗保健部门能够合并和分析大量基于健康的数据，从而通过做出更准确的预测来发现风险。因此，这项工作提出了一种心率实时预测系统，可帮助医疗服务提供者和患者实时避免心率风险。所提出的系统包括两个阶段，即离线阶段和在线阶段。离线阶段的目标是使用不同的预测技术来开发模型，以找到最低的均方根误差。心率时间序列数据集是从重症监护医学信息市场（MIMIC-II）中提取的。递归神经网络（RNN），长期短期记忆（LSTM），门控循环单位（GRU）和双向长期短期记忆（BI-LSTM）应用于心率时间序列。对于在线阶段，已使用Apache Kafka和Apache Spark根据最佳开发模型预先预测心率。根据实验结果，三层GRU记录了最佳性能。因此，三层GRU已被用于提前5分钟预测心率。和双向长期短期记忆（BI-LSTM）应用于心率时间序列。对于在线阶段，已使用Apache Kafka和Apache Spark根据最佳开发的模型预先预测心率。根据实验结果，三层GRU记录了最佳性能。因此，三层GRU已被用于提前5分钟预测心率。和双向长期短期记忆（BI-LSTM）应用于心率时间序列。对于在线阶段，已使用Apache Kafka和Apache Spark根据最佳开发的模型预先预测心率。根据实验结果，三层GRU记录了最佳性能。因此，三层GRU已被用于提前5分钟预测心率。