A heart failure classification model from radial artery pulse wave using LSTM neural networks

Heart failure (HF) represents a pressing global health issue demanding innovative and accessible approaches for early detection. Non-invasive, rapid, and cost-effective techniques utilizing deep learning (DL) hold significant promise for addressing this challenge.

This study included 462 participants categorized into healthy, coronary artery disease (CAD), and HF groups. Raw radial artery pulse wave data underwent preprocessing, including denoising, normalization, and SMOTE-based balancing. Four deep learning algorithms adept at handling sequential data - Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Bidirectional Long Short-Term Memory (Bi-LSTM) - were subsequently applied and evaluated for their classification performance. A 10-fold cross-validation strategy was employed to ensure a robust evaluation of model performance and stability.

To robustly evaluate performance and stability, a 10-fold cross-validation was performed. The LSTM model yielded the highest mean accuracy (0.8595 ± 0.0522) and demonstrated strong performance across other key metrics, including a high Area Under the Curve, establishing it as the most effective model in this study. To enhance model interpretability, the SHAP (SHapley Additive exPlanations) framework was utilized to determine global feature importance and explain the final LSTM model's predictions.

Our findings strongly suggest that an LSTM-based model analyzing radial artery pulse waves can effectively differentiate between healthy, CAD, and HF states. This approach represents a simple, non-invasive, and cost-effective methodology with significant potential as a valuable strategy to aid in the early screening and detection of HF. Further investigation and broader clinical validation are warranted to confirm the robustness and real-world applicability of this promising tool.