Detection algorithm of paroxysmal atrial fibrillation with sparse coding based on Riemannian manifold

针对阵发性房颤早期出现很短的初次发作较难被检出的问题，本文提出了一种基于黎曼流形稀疏编码的检测算法。本文算法考虑到非线性流形几何结构更接近真实的特征空间结构，计算协方差矩阵用于表征心率变异性（RR 间期变化），使数据处于黎曼流形空间中。在流形上应用稀疏编码，将每个协方差矩阵表示为黎曼字典原子的稀疏线性组合，其中稀疏重建损失由仿射不变黎曼度量定义，黎曼字典由迭代的方式学习得到。本文算法与现有算法相比，使用较短心率变异性信号，计算简单且没有对参数的依赖，并取得了更优的预测精度。在 MIT-BIH 房颤数据库上最终分类结果为灵敏度 99.34%、特异度 95.41%、准确率 97.45%，同时在 MIT-BIH 窦性心律数据库中实现了 95.18% 的特异度。本文提出的高精度阵发性房颤检测算法在可穿戴设备的长期监测中具有潜在的应用前景。.; In order to solve the problem that the early onset of paroxysmal atrial fibrillation is very short and difficult to detect, a detection algorithm based on sparse coding of Riemannian manifolds is proposed. The proposed method takes into account that the nonlinear manifold geometry is closer to the real feature space structure, and the computational covariance matrix is used to characterize the heart rate variability (RR interval variation), so that the data is in the Riemannian manifold space. Sparse coding is applied to the manifold, and each covariance matrix is represented as a sparse linear combination of Riemann dictionary atoms. The sparse reconstruction loss is defined by the affine invariant Riemannian metric, and the Riemann dictionary is learned by iterative method. Compared with the existing methods, this method used shorter heart rate variability signal, the calculation was simple and had no dependence on the parameters, and the better prediction accuracy was obtained. The final classification on MIT-BIH AF database resulted in a sensitivity of 99.34%, a specificity of 95.41% and an accuracy of 97.45%. At the same time, a specificity of 95.18% was realized in MIT-BIH NSR database. The high precision paroxysmal atrial fibrillation detection algorithm proposed in this paper has a potential application prospect in the long-term monitoring of wearable devices.