Automated classification of electrocardiogram (ECG) signals is a useful tool for diagnosing and monitoring cardiovascular diseases. This study compares three traditional machine learning algorithms (Decision Tree Classifier, Random Forest Classifier, and Logistic Regression) and three deep learning models (Simple Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Complex CNN (ECGLens)) for the classification of ECG signals from the PTB-XL dataset, which contains 12-lead recordings from normal patients and patients with various cardiac conditions. The DL models were trained on raw ECG signals, allowing them to automatically extract discriminative features. Data augmentation using the Stationary Wavelet Transform (SWT) was applied to enhance model performance, increase the diversity of training samples, and preserve the essential characteristics of the ECG signals. The models were evaluated using multiple metrics, including accuracy, precision, recall, F1-score, and ROC-AUC. The ECG-Lens model achieved the highest performance, with 80% classification accuracy and a 90% ROC-AUC. These findings demonstrate that deep learning architectures, particularly complex CNNs substantially outperform traditional ML methods on raw 12-lead ECG data, and provide a practical benchmark for selecting automated ECG classification models and identifying directions for condition-specific model development.

翻译：心电图（ECG）信号的自动分类是诊断和监测心血管疾病的有效工具。本研究比较了三种传统机器学习算法（决策树分类器、随机森林分类器和逻辑回归）与三种深度学习模型（简单卷积神经网络、长短期记忆网络和复杂卷积神经网络ECG-Lens），用于对PTB-XL数据集中来自正常患者及多种心脏疾病患者的12导联心电图记录进行分类。深度学习模型基于原始ECG信号进行训练，使其能够自动提取判别性特征。通过应用平稳小波变换进行数据增强，以提升模型性能、增加训练样本多样性并保留ECG信号的关键特征。模型采用准确率、精确率、召回率、F1分数和ROC-AUC等多重指标进行评估。ECG-Lens模型表现最优，分类准确率达80%，ROC-AUC达90%。研究结果表明，深度学习架构（尤其是复杂CNN）在原始12导联ECG数据上显著优于传统机器学习方法，这为选择自动化ECG分类模型及确定特定疾病模型开发方向提供了实用基准。