A Heart Failure Classification Model from Radial Artery Pulse Wave Using LSTM Neural Networks

Background The pressing global health issue of heart failure (HF) demands innovative approaches for early detection. Non-invasive, rapid, and cost-effective deep learning (DL)-based techniques offer a promising avenue for addressing this challenge. Methods A total of 462 participants were categorized into three groups: healthy, coronary artery disease (CAD), and heart failure (HF). Raw radial artery pulse wave data were collected from each participant, followed by preprocessing steps including denoising, normalization, and balancing. Subsequently, four deep learning (DL) algorithms were applied to the processed data: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Bidirectional Long Short-Term Memory (Bi-LSTM). Results LSTM achieved the highest classification performance, with an accuracy of 0.8587, precision of 0.87448, recall of 0.82164, F1-score of 0.83773, specificity of 0.92369, and AUC of 0.93365. Given its superior performance across all metrics, LSTM emerges as the preferred DL model for this study. Conclusion By employing LSTM to analyze radial artery pulse wave, we can accurately distinguish between healthy individuals, patients with CAD, and those with HF. This simple, non-invasive, and cost-effective method presents a potential strategy for early detection of HF.