Journal article 433 views 141 downloads
An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification
Ehab Essa,
Xianghua Xie 
IEEE Access, Volume: 9, Pages: 103452 - 103464
Swansea University Author:
Xianghua Xie
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DOI (Published version): 10.1109/access.2021.3098986
Abstract
An automatic system for heart arrhythmia classification can perform a substantial role inmanaging and treating cardiovascular diseases. In this paper, a deep learning-based multi-model system is proposed for the classification of electrocardiogram (ECG) signals. Two different deep learning bagging m...
| Published in: | IEEE Access |
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| ISSN: | 2169-3536 |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa57528 |
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| Abstract: |
An automatic system for heart arrhythmia classification can perform a substantial role inmanaging and treating cardiovascular diseases. In this paper, a deep learning-based multi-model system is proposed for the classification of electrocardiogram (ECG) signals. Two different deep learning bagging models are introduced to classify heartbeats into different arrhythmias types. The first model (CNN-LSTM) is based on a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) network to capture local features and temporal dynamics in the ECG data. The second model (RRHOS-LSTM) integrates some classical features, i.e. RR intervals and higher-order statistics (HOS), with LSTM model to effectively highlight abnormality heartbeats classes. We create a bagging model from the CNN-LSTM and RRHOS-LSTM networks by training each model on a different sub-sampling dataset to handle the high imbalance distribution of arrhythmias classes in the ECG data. Each model is also trained using a weighted loss function to provide high weight for not sufficiently represented classes. These models are then combined using a meta-classifier to form a strong coherent model. The meta-classifier is a feedforward fully connected neural network that takes the different predictions of bagging models as an input and combines them into a final prediction. The result of the meta-classifier is then verified by another CNN-LSTM model to decrease the false positive of the overall system. The experimental results are acquired by evaluating the proposed method on ECG data from the MIT-BIH arrhythmia database. The proposedmethod achieves an overall accuracy of 95.81% in the ‘‘subject-oriented’’ patient independent evaluation scheme. The averages of F1 score and positive predictive value are higher than all other methods by more than 3% and 8% respectively. The experimental results show the superiority of the proposed method for ECG heartbeats classification compared to many state-of-the-art methods. |
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| College: |
Faculty of Science and Engineering |
| Funders: |
Serˆ Cymru COFUND Fellowship |
| Start Page: |
103452 |
| End Page: |
103464 |