This study investigates the effectiveness of advanced deep learning models, including LSTM and transformer-based architectures, in predicting heart rate time series from the MIT-BIH Database. Traditional models like ARIMA and Prophet are limited by their need for manual parameter tuning and challenges in handling noisy, sparse, and highly variable medical data. The research compares these traditional models with deep learning models, particularly PatchTST and iTransformer, to assess their performance in capturing complex patterns and dependencies in heart rate data.
The study finds that deep learning models significantly outperform traditional models across multiple metrics, demonstrating their ability to handle the complexity and non-linearity of cardiovascular data more effectively. Transformer-based models, especially PatchTST, show superior performance, achieving significantly lower Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values. This highlights the potential of deep learning in enhancing patient monitoring and cardiovascular disease management, offering substantial clinical benefits.
The research also discusses the limitations of the study, such as the small dataset size and the focus on heart rate alone, and suggests future directions for extending these findings to larger, more diverse datasets and real-world clinical applications. The results underscore the importance of accurate predictions in optimizing patient health management and clinical interventions, setting a benchmark for future research and practical applications in health monitoring.This study investigates the effectiveness of advanced deep learning models, including LSTM and transformer-based architectures, in predicting heart rate time series from the MIT-BIH Database. Traditional models like ARIMA and Prophet are limited by their need for manual parameter tuning and challenges in handling noisy, sparse, and highly variable medical data. The research compares these traditional models with deep learning models, particularly PatchTST and iTransformer, to assess their performance in capturing complex patterns and dependencies in heart rate data.
The study finds that deep learning models significantly outperform traditional models across multiple metrics, demonstrating their ability to handle the complexity and non-linearity of cardiovascular data more effectively. Transformer-based models, especially PatchTST, show superior performance, achieving significantly lower Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values. This highlights the potential of deep learning in enhancing patient monitoring and cardiovascular disease management, offering substantial clinical benefits.
The research also discusses the limitations of the study, such as the small dataset size and the focus on heart rate alone, and suggests future directions for extending these findings to larger, more diverse datasets and real-world clinical applications. The results underscore the importance of accurate predictions in optimizing patient health management and clinical interventions, setting a benchmark for future research and practical applications in health monitoring.