Continuous Blood Pressure Prediction: A Time Series Approach for Enhancing Cardiovascular Health Monitoring
Keywords:
Convolutional Neural Networks (CNNs), CNN-LSTM Hybrid Model, Long Short-Term Memory (LSTM), Predictive Modeling, Temporal Pattern Recognition, , Time Series Data.Abstract
Blood pressure prediction is a critical task in healthcare, enabling proactive monitoring and intervention to prevent cardiovascular complications. In this paper, we introduce a cutting-edge hybrid model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for accurate blood pressure prediction using time-series data. The CNN component is leveraged for feature extraction and capturing spatial dependencies, while the LSTM component excels in learning temporal patterns and long-range dependencies in sequential data. Our experimental results demonstrate that the CNN-LSTM hybrid model outperforms the standalone CNN and LSTM models in terms of both accuracy and error metrics. Specifically, the hybrid model achieved an R² score of 0.9197, surpassing those of the CNN (R² = 0.8965) and LSTM (R² = 0.9125) models. The hybrid model also exhibited a lower Mean Squared Error (MSE) of 0.0042 and Root Mean Squared Error (RMSE) of 0.0649 compared to the CNN (MSE = 0.0053, RMSE = 0.0727) and LSTM (MSE = 0.0045, RMSE = 0.0672) models. These results underscore the performance of the CNN-LSTM hybrid model for predicting blood pressure from time-series data, offering a promising solution for improving predictive accuracy in healthcare applications. The proposed model has the capability to enhance continuous monitoring systems, ultimately contributing to better patient outcomes through timely intervention.
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