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Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers

Haowei Ni, Shuchen Meng, Xieming Geng, Panfeng Li, Zhuoying Li, Xupeng Chen, Xiaotong Wang, Shiyao Zhang

TL;DR

Investigation of advanced deep learning models, including LSTM, and transformer-based architectures, for predicting heart rate time series from the MIT-BIH Database demonstrates that deep learning models, particularly PatchTST, significantly outperform traditional models across multiple metrics, capturing complex patterns and dependencies more effectively.

Abstract

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.

Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers

TL;DR

Investigation of advanced deep learning models, including LSTM, and transformer-based architectures, for predicting heart rate time series from the MIT-BIH Database demonstrates that deep learning models, particularly PatchTST, significantly outperform traditional models across multiple metrics, capturing complex patterns and dependencies more effectively.

Abstract

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.
Paper Structure (15 sections, 3 figures, 1 table)

This paper contains 15 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Data Description and Preparation
  4. Framework
  5. Time Series Models Training
  6. Performance Evaluation
  7. Experiments
  8. Benchmark Model Performance
  9. Deep Learning Based Model Performance
  10. Interpretation of Results
  11. Discussion
  12. Impact of Transformer-based models on Time Series Prediction Performance
  13. Theoretical and Practical Implications
  14. Study Limitations and Future Research Directions
  15. Conclusion

Figures (3)

  • Figure 1: T1 Heart Rate time series.
  • Figure 2: Prophet fit over the entire T1 dataset. The actual fit is depicted in light blue, the trend function is represented by a red line, and the trend changepoints are marked by red dashed vertical lines.
  • Figure 3: Comparison of True Values vs. Predicted Outputs from five best models