An Automated Embedded Distribution of Deep Learning Heart Disease Identification System Using ECG Signal
Keywords:
Cardiovascular Disease (CVD), Electrocardiogram (ECG), Multi-Proportional Peak Pattern (MPPP), Embedded Distribution of Deep Learning (ED-DL)Abstract
The article relies on improvements in feature extraction and investigates successful ECG recognition that can be achieved by integrating the Multi-Proportional Peak Pattern (MPPP)-based feature learning model with the Embedded Distribution of Deep Learning (ED-DL) method for the classification of features extracted from the proposed work. The ECG signal texture extraction technique generates the pattern of structural information in an ECG signal and provides instructions for each block to determine the heart condition that matches the feature database. The multi-proportional peak pattern method improves the feature extraction model by extracting the optimal combination of features at different angles of the projection plane to obtain the clear characteristics of a disease. A good collection of feature vectors is also extracted using an MPPP-based feature extractor. The ED-DL approach is then incorporated for the categorization of extracted characteristic features. The suggested model is subjected to a comparative result analysis to demonstrate its superiority compared with Gated Recurrent Unit-Extreme Learning Machine (GRU-ELM) and Class Imbalanced Gated Recurrent Unit-Extreme Learning Machine (CIGRU-ELM) techniques in terms of performance evaluation metrics. An average accuracy, sensitivity, specificity, and F1-score of 93.6%, 96.3%, 93.8%, and 94.5%, respectively, and an error rate of 6.4% have been obtained in classifying several classes, namely coronary artery disease (CAD), myocardial infarction (MI), congestive heart failure (CHF), cardiomyopathy, and normal class.
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