本项目聚焦于心电（ECG）心律失常的精准分类，旨在利用深度学习技术开发一套高效的分类系统。鉴于 ECG 数据的复杂性，包括心电波形的复杂性、异常波形出现位置的不确定性以及数据维度大等特点，项目采用 CNN 与 LSTM 相结合的模型。通过 CNN 提取 ECG 信号的局部形态特征，LSTM 捕捉长期时序依赖关系，两者协同工作，实现对心律失常的准确分类。项目涉及数据处理、模型构 建、训练优化以及结果评估等全流程工作，最终构建出具有高准确率的分类模型，为心律失常的辅助诊断提供有力支持 。
☆11Jan 21, 2026Updated 8 months ago
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