Abstract
Automatic extraction of relevant and reliable information from electrocardiogram (ECG) signals is essential for heart disease diagnosis and treatment. This study proposes deep learning model based on improved one-dimensional convolutional neural network (1D-CNN) architecture for classifying heart disease using ECG data. First, we collect ECG recordings from patients with and without heart disease. Then, the relevant features are extracted from the ECG data, which is a critical step as the features’ quality directly impacts the predictive models’ performance. Next, we apply the predictive models, encompassing 1D-CNN, Support Vector Machine (SVM), and Logistic Regression, combined with fine-tuned hyperparameters and StandardScaler, to improve heart disease prediction performance. The experimental results show that the proposed deep learning model using 1D-CNN combined with fine-tuning hyperparameters and StandardScaler can achieve better classification results on ECG-based heart disease classification tasks than previous studies.
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Nguyen, H.T., Cao, A.H., Bui, P.H.D. (2023). Electrocardiogram-Based Heart Disease Classification with Machine Learning Techniques. In: Nguyen, N.T., et al. Advances in Computational Collective Intelligence. ICCCI 2023. Communications in Computer and Information Science, vol 1864. Springer, Cham. https://doi.org/10.1007/978-3-031-41774-0_54
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