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。

应用一维卷积神经网络进行冠状动脉疾病分类

Coronary Artery Disease Classification Using One-dimensional  Convolutional Neural Network

By identifying similarities between successive inputs, Self-Supervised
Learning (SSL) methods for time series analysis have demonstrated their
effectiveness in encoding the inherent static characteristics of temporal data.
However, an exclusive emphasis on similarities might result in representations
that overlook the dynamic attributes critical for modeling cardiovascular
diseases within a confined subject cohort. Introducing Distilled Encoding
Beyond Similarities (DEBS), this paper pioneers an SSL approach that transcends
mere similarities by integrating dissimilarities among positive pairs. The
framework is applied to electrocardiogram (ECG) signals, leading to a notable
enhancement of +10\% in the detection accuracy of Atrial Fibrillation (AFib)
across diverse subjects. DEBS underscores the potential of attaining a more
refined representation by encoding the dynamic characteristics of time series
data, tapping into dissimilarities during the optimization process. Broadly,
the strategy delineated in this study holds the promise of unearthing novel
avenues for advancing SSL methodologies tailored to temporal data.

通过整合正样本之间的不同之处，本研究的自我监督学习方法 (DEBS) 在编码时间序列数据的固有静态特征方面表现出色，强调通过优化过程中的不同之处来实现对动态特征的更精细表达，应用于心电图 (ECG) 信号后能够显著提高房颤 (AFib) 的检测准确率，为推进适用于时间序列数据的自我监督学习方法开辟新的途径。