Real-Time System Prediction for Heart Rate Using Deep Learning and Stream Processing Platforms
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 str...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5535734 |
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| author | Abdullah Alharbi Wael Alosaimi Radhya Sahal Hager Saleh |
| author_facet | Abdullah Alharbi Wael Alosaimi Radhya Sahal Hager Saleh |
| author_sort | Abdullah Alharbi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-11d7b541ff6c4d1886f7c93bb795c56f |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-11d7b541ff6c4d1886f7c93bb795c56f2025-08-20T02:01:53ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55357345535734Real-Time System Prediction for Heart Rate Using Deep Learning and Stream Processing PlatformsAbdullah Alharbi0Wael Alosaimi1Radhya Sahal2Hager Saleh3Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaFaculty of Computer Science and Engineering, Hodeidah University, Al Hudaydah, YemenFaculty of Computers and Artificial Intelligence, South Valley University, Hurghada, EgyptLow 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.http://dx.doi.org/10.1155/2021/5535734 |
| spellingShingle | Abdullah Alharbi Wael Alosaimi Radhya Sahal Hager Saleh Real-Time System Prediction for Heart Rate Using Deep Learning and Stream Processing Platforms Complexity |
| title | Real-Time System Prediction for Heart Rate Using Deep Learning and Stream Processing Platforms |
| title_full | Real-Time System Prediction for Heart Rate Using Deep Learning and Stream Processing Platforms |
| title_fullStr | Real-Time System Prediction for Heart Rate Using Deep Learning and Stream Processing Platforms |
| title_full_unstemmed | Real-Time System Prediction for Heart Rate Using Deep Learning and Stream Processing Platforms |
| title_short | Real-Time System Prediction for Heart Rate Using Deep Learning and Stream Processing Platforms |
| title_sort | real time system prediction for heart rate using deep learning and stream processing platforms |
| url | http://dx.doi.org/10.1155/2021/5535734 |
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