Deep Learning Guide Task Dataset

DATASET FOR: Deep Learning for Atrial Fibrillation Classification Using Single-Lead ECG This educational project introduces students to deep learning through a biomedical engineering lens. The objective is to build a binary classifier that distinguishes between normal heart rhythm and atrial fibrillation using one-lead ECG signals. Important Note: The dataset used in this task is based on real patient data but has been augmented for instructional purposes. IT IS NOT SUITABLE FOR ACADEMIC RESEARCH OR CLINICAL USE! The data has also be normalised and preprocessed for direct deep learning use. Labels: - 0 = Normal (Control) - 1 = Atrial Fibrillation数据集用于：基于单导联心电图（Electrocardiogram，ECG）的心房颤动分类深度学习研究 本教学项目以生物医学工程视角为切入点，向学生讲授深度学习知识。 本项目的目标为构建二分类器，利用单导联ECG信号区分正常心律与心房颤动。 重要提示： 本任务所使用的数据集基于真实患者数据，但为适配教学用途已进行数据增强处理。 本数据集不可用于学术研究或临床应用！ 该数据已完成归一化与预处理，可直接用于深度学习任务。 标签说明： - 0 = 正常心律（对照组） - 1 = 心房颤动