A three-level risk stratification algorithm for arrhythmia based on CNN-LSTM palindromic structure

Cardiovascular diseases are characterized by sudden onset, high mortality rates, and high recurrence rates, making early screening and timely intervention essential. However, due to insufficient medical resources, real-time and refined electrocardiogram monitoring of patients is difficult to implement. Meanwhile, the complex spatiotemporal features of electrocardiogram (ECG) signals bring challenges to multidimensional feature mining and accurate recognition. Therefore, in this study, we propose a three-level risk stratification algorithm for arrhythmia, which not only ensures precise intervention for different types of arrhythmias to achieve optimized allocation of medical resources but also comprehensively captures the spatiotemporal features of ECG signals for high-accuracy risk stratification detection. The algorithm first proposes a three-level arrhythmia risk stratification scheme, classifying cases as Normal, Not Life-threatening, and Life-threatening, with corresponding intervention measures of no need for monitoring, close attention, and immediate intervention, respectively. The core model of this algorithm is a deep learning fusion network based on the palindromic structure of Convolutional Neural Network (CNN) and Long Short-Term Memory network (LSTM), which enables precise three-level risk detection for all types of arrhythmias. Experimental results demonstrate that the algorithm exhibits favorable generalization performance across multiple datasets. It not only outperforms standalone CNN and LSTM models but also surpasses classical classifiers. Notably, on the 2-second ECG segments from the gold-standard dataset, it achieves an accuracy of 99.68% and a Specificity of 99.65%.The 10-fold cross-validation yields an accuracy of 99.62% ± 0.09% for 3-second segments, and patient-level validation on 10-second segments achieves an accuracy of 96.5%. The satisfactory results across varied segment lengths verify the reliability and practicality of the proposed method. In both arrhythmia risk stratification and detection tasks, the algorithm presents moderate competitive advantages.