基于希尔伯特黄变换和深度卷积神经网络的房颤检测

房颤是一种常见的心律失常，其发病率会随着年龄增长而升高。因此，从心电(ECG)信号中尽早识别出房颤，有助于降低中风风险和心源性死亡率。为有效提高其检测准确率，该文提出一种基于希尔伯特黄变换(HHT)和深度卷积神经网络的房颤检测方法。1维的时域心电信号通过希尔伯特黄变换，转换为时频域信号，旨在通过时频分析，丰富原始信号的特征。进而，采用DenseNet深度卷积神经网络来处理精细的时频图，并在迭代过程中选出最佳检测模型。该方法获得的最佳检测模型在麻省理工学院-贝斯以色列医院(MIT-BIH)和2017年生理信号竞赛(2017 PhysioNet Challenge)的房颤数据集上分别取得了99.11%和97.25%的检测准确率。此外，该文将希尔伯特黄变换与其他时频分析方法以及稠密网络(DenseNet)与其他卷积神经网络进行了对比。相比于其他检测方法，实验结果表明希尔伯特黄变换和深度卷积神经网络(DCNN)为房颤检测提供了更加准确的识别方式。

Atrial Fibrillation Detection Based on Hilbert-Huang Transform and Deep Convolutional Neural Network

Atrial fibrillation is a common arrhythmia and its morbidity increases with age. Thus, stroke risk and cardiogenic mortality can be significantly reduced by early atrial fibrillation detection from ElectroCardioGram (ECG). In order to improve effectively detection accuracy, a novel approach is proposed to detect atrial fibrillation based on Hilbert-Huang Transform(HHT) and deep convolutional neural network. HHT is employed to transform electrocardiogram from time domain to time-frequency domain so as to enrich the feature of original data. Following that, DenseNet is introduced to deal with the detailed graph and the best model is selected during the iteration. The optimal model obtained by the proposed method achieves 99.11% and 97.25% accuracy respectively on the Massachusetts Institute of Technology - Beth Israel Hospital(MIT-BIH) and 2017 PhysioNet Challenge atrial fibrillation databases. In addition, HHT and DenseNet are compared with other time-frequency analysis and convolutional neural networks, respectively. Compared with some existing methods, the results proved that atrial fibrillation detection by HHT and Deep Convolutional Neural Network(DCNN) obtains a high detection performance.