  Coronary Artery Disease (CAD) diagnostic to be a major global cause of death,
necessitating innovative solutions. Addressing the critical importance of early
CAD detection and its impact on the mortality rate, we propose the potential of
one-dimensional convolutional neural networks (1D-CNN) to enhance detection
accuracy and reduce network complexity. This study goes beyond traditional
diagnostic methodologies, leveraging the remarkable ability of 1D-CNN to
interpret complex patterns within Electrocardiogram (ECG) signals without
depending on feature extraction techniques. We explore the impact of varying
sample lengths on model performance and conduct experiments involving layers
reduction. The ECG data employed were obtained from the PhysioNet databases,
namely the MIMIC III and Fantasia datasets, with respective sampling
frequencies of 125 Hz and 250 Hz. The highest accuracy for unseen data obtained
with a sample length of 250. These initial findings demonstrate the potential
of 1D-CNNs in CAD diagnosis using ECG signals and highlight the sample size's
role in achieving high accuracy.
