Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive Healthcare
In the rapidly evolving field of medical diagnosis, the accurate and prompt interpretation of heartbeat electrocardiogram (ECG) signals have become increasingly crucial. Despite the presence of recent advances, there is an exigent need to enhance the accuracy of existing methodologies, especially gi...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/11/18/3942 |
| _version_ | 1797578989274923008 |
|---|---|
| author | Njud S. Alharbi Hadi Jahanshahi Qijia Yao Stelios Bekiros Irene Moroz |
| author_facet | Njud S. Alharbi Hadi Jahanshahi Qijia Yao Stelios Bekiros Irene Moroz |
| author_sort | Njud S. Alharbi |
| collection | DOAJ |
| description | In the rapidly evolving field of medical diagnosis, the accurate and prompt interpretation of heartbeat electrocardiogram (ECG) signals have become increasingly crucial. Despite the presence of recent advances, there is an exigent need to enhance the accuracy of existing methodologies, especially given the profound implications such interpretations can have on patient prognosis. To this end, we introduce a novel ensemble comprising Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models to enable the enhanced classification of heartbeat ECG signals. Our approach capitalizes on LSTM’s exceptional sequential data learning capability and CNN’s intricate pattern recognition strength. Advanced signal processing methods are integrated to enhance the quality of raw ECG signals before feeding them into the deep learning model. Experimental evaluations on benchmark ECG datasets demonstrate that our proposed ensemble model surpasses other state-of-the-art deep learning models. It achieves a sensitivity of 94.52%, a specificity of 96.42%, and an accuracy of 95.45%, highlighting its superior performance metrics. This study introduces a promising tool for bolstering cardiovascular disease diagnosis, showcasing the potential of such techniques to advance preventive healthcare. |
| first_indexed | 2024-03-10T22:30:28Z |
| format | Article |
| id | doaj.art-e6c1b421d403482a8ca79c6f6069563b |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T22:30:28Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-e6c1b421d403482a8ca79c6f6069563b2023-11-19T11:49:44ZengMDPI AGMathematics2227-73902023-09-011118394210.3390/math11183942Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive HealthcareNjud S. Alharbi0Hadi Jahanshahi1Qijia Yao2Stelios Bekiros3Irene Moroz4Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi ArabiaInstitute of Electrical and Electronics Engineers, Toronto, ON M5V 3T9, CanadaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaDepartment of Banking and Finance, FEMA, University of Malta, MSD 2080 Msida, MaltaMathematical Institute, University of Oxford, Oxford OX2 6GG, UKIn the rapidly evolving field of medical diagnosis, the accurate and prompt interpretation of heartbeat electrocardiogram (ECG) signals have become increasingly crucial. Despite the presence of recent advances, there is an exigent need to enhance the accuracy of existing methodologies, especially given the profound implications such interpretations can have on patient prognosis. To this end, we introduce a novel ensemble comprising Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models to enable the enhanced classification of heartbeat ECG signals. Our approach capitalizes on LSTM’s exceptional sequential data learning capability and CNN’s intricate pattern recognition strength. Advanced signal processing methods are integrated to enhance the quality of raw ECG signals before feeding them into the deep learning model. Experimental evaluations on benchmark ECG datasets demonstrate that our proposed ensemble model surpasses other state-of-the-art deep learning models. It achieves a sensitivity of 94.52%, a specificity of 96.42%, and an accuracy of 95.45%, highlighting its superior performance metrics. This study introduces a promising tool for bolstering cardiovascular disease diagnosis, showcasing the potential of such techniques to advance preventive healthcare.https://www.mdpi.com/2227-7390/11/18/3942cardiovascular disease diagnosisensemble neural networktime series classificationconvolutional neural networkrecurrent neural network |
| spellingShingle | Njud S. Alharbi Hadi Jahanshahi Qijia Yao Stelios Bekiros Irene Moroz Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive Healthcare Mathematics cardiovascular disease diagnosis ensemble neural network time series classification convolutional neural network recurrent neural network |
| title | Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive Healthcare |
| title_full | Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive Healthcare |
| title_fullStr | Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive Healthcare |
| title_full_unstemmed | Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive Healthcare |
| title_short | Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive Healthcare |
| title_sort | enhanced classification of heartbeat electrocardiogram signals using a long short term memory convolutional neural network ensemble paving the way for preventive healthcare |
| topic | cardiovascular disease diagnosis ensemble neural network time series classification convolutional neural network recurrent neural network |
| url | https://www.mdpi.com/2227-7390/11/18/3942 |
| work_keys_str_mv | AT njudsalharbi enhancedclassificationofheartbeatelectrocardiogramsignalsusingalongshorttermmemoryconvolutionalneuralnetworkensemblepavingthewayforpreventivehealthcare AT hadijahanshahi enhancedclassificationofheartbeatelectrocardiogramsignalsusingalongshorttermmemoryconvolutionalneuralnetworkensemblepavingthewayforpreventivehealthcare AT qijiayao enhancedclassificationofheartbeatelectrocardiogramsignalsusingalongshorttermmemoryconvolutionalneuralnetworkensemblepavingthewayforpreventivehealthcare AT steliosbekiros enhancedclassificationofheartbeatelectrocardiogramsignalsusingalongshorttermmemoryconvolutionalneuralnetworkensemblepavingthewayforpreventivehealthcare AT irenemoroz enhancedclassificationofheartbeatelectrocardiogramsignalsusingalongshorttermmemoryconvolutionalneuralnetworkensemblepavingthewayforpreventivehealthcare |