Summary of Time Series Modeling For Heart Rate Prediction: From Arima to Transformers, by Haowei Ni and Shuchen Meng and Xieming Geng and Panfeng Li and Zhuoying Li and Xupeng Chen and Xiaotong Wang and Shiyao Zhang
Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers
by Haowei Ni, Shuchen Meng, Xieming Geng, Panfeng Li, Zhuoying Li, Xupeng Chen, Xiaotong Wang, Shiyao Zhang
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates advanced deep learning models for predicting heart rate time series from the MIT-BIH Database. Researchers compared traditional models like ARIMA and Prophet with LSTM and transformer-based architectures, finding that deep learning models significantly outperform traditional ones across multiple metrics. Specifically, PatchTST showed superior performance in capturing complex patterns and dependencies. This research highlights the potential of deep learning to enhance patient monitoring and cardiovascular disease (CVD) management, suggesting substantial clinical benefits. The study’s findings demonstrate the value of incorporating advanced machine learning techniques into CVD forecasting models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at new ways to predict heart rate patterns using special kinds of artificial intelligence called deep learning. The researchers tested these new methods against older ones and found that they do a much better job of capturing tricky patterns in the data. They used a big database of heart rate information from MIT-BIH to test their ideas. The results show that this new approach could be really helpful for monitoring patients with cardiovascular disease, which is a major cause of death worldwide. This study takes an important step towards using advanced technology to improve patient care. |
Keywords
* Artificial intelligence * Deep learning * Lstm * Machine learning * Time series * Transformer
