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...

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.