Abstract Background and purpose Myocardial infarction (MI), a widespread cardiovascular ailment, often lacks immediate patient awareness being early and accurate MI diagnosis crucial for lifesaving interventions. In artificial intelligence studies, ECG signals are extensively explored, while fewer examine the potential of vectorcardiogram (VCG) which offers a three-dimensional cardiac dipole representation. Traditional Machine Learning methods manually extract features; some studies advocate VCG features. Deep Learning, especially Convolutional Neural Networks (CNN), automates feature extraction. Although ECG contains redundant information from the VCG's 12 projections, no studies directly apply CNNs to VCG, nor assess the impact of redundancy on predictive ability. This study aims to bridge this gap by investigating CNNs' diagnostic capabilities with VCG inputs, specifically focusing on improving MI classification. Methods The PTB-XL database was used for this study, containing 10-seconds ECG data from various pathologies, including MI. The database encompasses 9,481 records from healthy patients and 4,129 records from patients with MI. Data was divided into 80% for training and 20% for testing. Consequently, the dataset was expanded to have 52% representing MI and 48% representing healthy cases. We designed a 1D-CNN classifier (Figure 1.A) with three convolutional layers. The network was trained using both ECG and VCG reconstruction through the Inverse Dower Transform. Results The trained model achieved accuracy values of 0.895 and 0.914 for the test set when utilizing ECG and VCG, respectively. These results underscore how the information condensation in the VCG contributes to an enhanced predictive capacity of the model. In a qualitative analysis, activation maps extracted from the CNN illustrate heightened activation in the QRS complex when the disease is present, contrasting with diminished activation in its absence. These maps depict the weight assigned to each signal segment, enabling the classification of signals into healthy and diseased categories. The presented figure exemplifies a notable activation in the QRS loop in the case of myocardial infarction (MI), aligning with the observed alteration in the cardiac signal associated with this pathology (Figure 1.B). Conclusion In this study, an interpretable CNN has been designed for MI detection, with the VCG yielding better results than the ECG for this task due to information compression. This provides a new insight for enhancing classification capability in Deep Learning models, allowing for advancements in the MI detection.
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