基于相空间重构的心冲击信号房颤检测方法

针对房颤事件中的节律异常特性，提出应用相空间重构算法提取心冲击(ballistocardiogram， BCG)信号的二维节律特征，并对重构过程中的最优嵌入维数和时间延迟参数进行了讨论.首先，将心脏搏动视为非线性动力学系统，应用相空间重构理论将一维时间序列映射到高维相空间中，从而获取BCG信号中表征房颤过程节律异常的相空间轨迹特征.其次，探讨了重构过程中适于房颤诊断的最优嵌入维数和时间延迟参数，并结合卷积神经网络实现了对房颤的智能诊断.最终，通过对59名受试者提取到的2000组BCG数据进行十折交叉验证，所提方法的分类准确率达到91.00%，与基于经典时频特征的机器学习方法相比较，有较为明显的提高，从而验证了所提方法的优越性.

Atrial Fibrillation Detection Method Based on Phase Space Reconstruction Using Ballistocardiogram Signal

Based on the arrhythmia characteristics of atrial fibrillation(AF)， phase space reconstruction(PSR)is applied to extract the 2D rhythmic feature of the ballistocardiogram(BCG)signal. In the process of reconstruction， the optimal embedding dimensions and time delay parameters are discussed. Firstly， the heart beat is regarded as a non-linear dynamic system， and the 1D time series is mapped to the high-dimensional phase space， based on the phase space reconstruction theory， to obtain the 2D trajectory， which describes the abnormal rhythm of AF in BCG signal. Secondly， the optimal embedding dimension and time delay parameters of the reconstruction procedure for AF diagnosis are discussed， and the convolutional neural network(CNN) is applied to identify AF automatically. Finally， 2000 BCG segments from 59 subjects are used to validate the classification performance. The accuracy reaches 91.00% by means of the tenfold cross-validation. Compared to the machine learning method based on the classical time-frequency features， the accuracy is improved， which verifies the superiority of the proposed method.