A Cost-Based Dual ConvNet-Attention Transfer Learning Model for ECG Heartbeat Classification

Abstract

The heart is a very crucial organ of the body. Concerted efforts are constantly put forward to provide adequate monitoring of the heart. A heart disorder is reported to cause a lot of hidden ailments resulting in numerous deaths. Early heart monitoring using an electrocardiogram (ECG) through the advancement of computer-aided diagnostic (CAD) systems is widely used. Meanwhile, the use of human reading of ECG results are faced with many challenges of inaccurate and unreliable interpretations. Over two decades, studies provided artificial intelligence (AI) technique using machine learning (ML) algorithms as a fast and reliable technique for ECG heartbeat classification. Moreover, in recent times, deep learning (DL) techniques have been focused on providing automatic feature extraction and better classification performance. On the other hand, the challenge with the ECG data is its imbalance nature. Therefore, this paper proposes a cost-based dual convolutional attention transfer DL model for ECG classification. The proposed model uses PhysionNet-MIT-BIH and Physikalisch-Technische Bundesanstalt (PTB) Diagnostics datasets. The first part uses the MIT-BIH for ECG categorization, while representations learned from the first classifier are used for PTB analysis through transfer learning (TL). The proposed model is evaluated and compared with well-performing conventional ML models based on their F1-score and accuracy scores. Our experimental finding show that the proposed model outperformed the well-performing ML models as well as competitive with past studies for both the classification and TL part, having obtained 98.45% for both F1-score and accuracy. The proposed model is applicable to real-life trials and experiments for ECG heartbeat and other similar domains.

Machine Learning-Driven Stroke Prediction Using Independent Dataset

“…The stroke forecast in recurrence within a one-year prediction window has an accuracy of 88%, a positive predictive value of 42%, and a specificity of 96% using Random Forest (RF) with up-sampling of the training dataset. On the other hand, Victor et al [12] created a costeffective solution to the imbalanced data problem associated with ECG datasets. The technique penalizes the minority class using class-imbalance-ratio-weight which utilizes the suggested model loss function without additional expense, attaining model generalizability.…”