Cardiovascular disease (CVD) is a leading cause of death globally, necessitating precise forecasting models for monitoring vital signs like heart rate, blood pressure, and ECG. Traditional models, such as ARIMA and Prophet, are limited by their need for manual parameter tuning and challenges in handling noisy, sparse, and highly variable medical data. This study investigates advanced deep learning models, including LSTM, and transformer-based architectures, for predicting heart rate time series from the MIT-BIH Database. Results demonstrate that deep learning models, particularly PatchTST, significantly outperform traditional models across multiple metrics, capturing complex patterns and dependencies more effectively. This research underscores the potential of deep learning to enhance patient monitoring and CVD management, suggesting substantial clinical benefits. Future work should extend these findings to larger, more diverse datasets and real-world clinical applications to further validate and optimize model performance.

这项研究探讨了使用高级深度学习模型（如LSTM和基于transformer的架构）从MIT-BIH数据库预测心率时间序列的方法，结果表明，深度学习模型（尤其是PatchTST）在多个指标上显著优于传统模型，更有效地捕捉复杂的模式和依赖关系，强调了深度学习在改善患者监测和心血管疾病管理方面的潜力，并提出了实质性的临床益处，未来的工作应该将这些发现扩展到更大、更多样化的数据集和真实世界的临床应用中以进一步验证和优化模型性能。

心率预测的时间序列建模：从ARIMA到Transformer