Open Access

ARTICLE

Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

S. Karthik¹, M. Santhosh^1,*, M. S. Kavitha¹, A. Christopher Paul²

1 Department of Computer Science & Engineering, SNS College of Technology, Coimbatore, 641035, India
2 Department of Information Technology, Karpagam Institute of Technology, Coimbatore, 641105, India

* Corresponding Author: M. Santhosh. Email:

Computer Systems Science and Engineering 2022, 42(1), 183-199. https://doi.org/10.32604/csse.2022.021698

Received 10 July 2021; Accepted 11 August 2021; Issue published 02 December 2021

Abstract

Automated biomedical signal processing becomes an essential process to determine the indicators of diseased states. At the same time, latest developments of artificial intelligence (AI) techniques have the ability to manage and analyzing massive amounts of biomedical datasets results in clinical decisions and real time applications. They can be employed for medical imaging; however, the 1D biomedical signal recognition process is still needing to be improved. Electrocardiogram (ECG) is one of the widely used 1-dimensional biomedical signals, which is used to diagnose cardiovascular diseases. Computer assisted diagnostic models find it difficult to automatically classify the 1D ECG signals owing to time-varying dynamics and diverse profiles of ECG signals. To resolve these issues, this study designs automated deep learning based 1D biomedical ECG signal recognition for cardiovascular disease diagnosis (DLECG-CVD) model. The DLECG-CVD model involves different stages of operations such as pre-processing, feature extraction, hyperparameter tuning, and classification. At the initial stage, data pre-processing takes place to convert the ECG report to valuable data and transform it into a compatible format for further processing. In addition, deep belief network (DBN) model is applied to derive a set of feature vectors. Besides, improved swallow swarm optimization (ISSO) algorithm is used for the hyperparameter tuning of the DBN model. Lastly, extreme gradient boosting (XGBoost) classifier is employed to allocate proper class labels to the test ECG signals. In order to verify the improved diagnostic performance of the DLECG-CVD model, a set of simulations is carried out on the benchmark PTB-XL dataset. A detailed comparative study highlighted the betterment of the DLECG-CVD model interms of accuracy, sensitivity, specificity, kappa, Mathew correlation coefficient, and Hamming loss.

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Citations