Abstract
Automatic biomedical signal recognition is an important process for several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patterns of the ECG signals. In order to raise the diagnostic accuracy and reduce the diagnostic time, automated computer aided diagnosis model is necessary. With the advancements of artificial intelligence (AI) techniques, large quantity of biomedical datasets can be easily examined for decision making. In this aspect, this paper presents an intelligent biomedical ECG signal processing (IBECG-SP) technique for CVD diagnosis. The proposed IBECG-SP technique examines the ECG signals for decision making. In addition, gated recurrent unit (GRU) model is used for the feature extraction of the ECG signals. Moreover, earthworm optimization (EWO) algorithm is utilized to optimally tune the hyperparameters of the GRU model. Lastly, softmax classifier is employed to allot appropriate class labels to the applied ECG signals. For examining the enhanced outcomes of the proposed IBECG-SP technique, an extensive simulation analysis take place on the PTB-XL database. The experimental results portrayed the supremacy of the IBECG-SP technique over the recent state of art techniques.
Highlights
Cardiovascular diseases (CVD) is a worldwide healthcare problem since they contribute around 30% of overall death and 10% burden of the overall disease [1]
This paper introduces an intelligent biomedical ECG signal processing (IBECG-SP) technique for CVD diagnosis
This study has focused on the design of IBECG-SP technique for ECG signal classification
Summary
Cardiovascular diseases (CVD) is a worldwide healthcare problem since they contribute around 30% of overall death and 10% burden of the overall disease [1]. The WHO determined CVD from enzymes, symptoms, and ECG abnormalities [5] Among these diagnoses approaches, ECG can potentially deliver cost saving, since the signals attainment is non invasive needs only medium qualified labor. ECG alone is frequently inadequate for diagnosing CVDs, like acute myocardial infarction/acute coronary syndrome It is significant for improving the accuracy of ECG based diagnoses since it decreases the requirement for high cost diagnoses tools. A constraint of various processes is utilized for automate classification of ECG is the incapability of handling huge intraclass variants They are heavily based on supervised training datasets and carry out poorly while processing huge amounts of novel ECG records [7].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
