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.

翻译：心血管疾病是全球范围内主要的死亡原因，因此需要精确的预测模型来监测心率、血压和心电图等生命体征。传统模型，如ARIMA和Prophet，受限于其需要手动参数调优以及处理噪声大、稀疏且高度变化的医疗数据时所面临的挑战。本研究探讨了先进的深度学习模型，包括LSTM和基于Transformer的架构，用于基于MIT-BIH数据库预测心率时间序列。结果表明，深度学习模型，尤其是PatchTST，在多项指标上显著优于传统模型，能更有效地捕捉复杂的模式和依赖关系。本研究强调了深度学习在增强患者监测和心血管疾病管理方面的潜力，预示着显著的临床效益。未来的工作应将此发现扩展到更大、更多样化的数据集以及现实世界的临床应用中，以进一步验证和优化模型性能。