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.

冠状动脉疾病诊断的一维卷积神经网络（1D-CNN）通过提高检测准确性和减少网络复杂性的潜力来解决早期诊断对人类死亡率的重要影响问题。通过解释心电图（ECG）信号中复杂模式的能力，无需依赖特征提取技术，这项研究超越了传统诊断方法。我们研究了样本长度对模型性能的影响，并进行了相关实验，包括降低网络层数。使用的 ECG 数据来自 PhysioNet 数据库，分别是 MIMIC III 和 Fantasia 数据集，采样频率分别为 125 Hz 和 250 Hz。在样本长度为 250 时，获得了对未知数据最高的准确性。这些初步发现展示了使用 ECG 信号进行冠状动脉疾病诊断的 1D-CNN 的潜力，并强调了样本大小在实现高准确性方面的作用。