Automatic classification of electrocardiogram (ECG) signals is important for diagnosing heart arrhythmias. A big challenge in automatic ECG classification is the variation in the waveforms and characteristics of ECG signals among different patients. To address this issue, this paper proposes adapting a patient-independent deep neural network (DNN) using the information in the patient-dependent identity vectors (i-vectors). The adapted networks, namely i-vector adapted patient-specific DNNs (iAP-DNNs), are tuned toward the ECG characteristics of individual patients. For each patient, his/her ECG waveforms are compressed into an i-vector using a factor analysis model. Then, this i-vector is injected into the middle hidden layer of the patient-independent DNN. Stochastic gradient descent is then applied to fine-tune the whole network to form a patient-specific classifier. As a result, the adaptation makes use of not only the raw ECG waveforms from the specific patient but also the compact representation of his/her ECG characteristics through the i-vector. Analysis on the hidden-layer activations shows that by leveraging the information in the i-vectors, the iAP-DNNs are more capable of discriminating normal heartbeats against arrhythmic heartbeats than the networks that use the patient-specific ECG only for the adaptation. Experimental results based on the MIT-BIH database suggest that the iAP-DNNs perform better than existing patient-specific classifiers in terms of various performance measures. In particular, the sensitivity and specificity of the existing methods are all under the receiver operating characteristic curves of the iAP-DNNs.

中文翻译：

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基于I矢量的深度神经网络患者适应性自动心跳分类

心电图（ECG）信号的自动分类对于诊断心律不齐很重要。自动ECG分类的一大挑战是不同患者之间ECG信号的波形和特征的变化。为了解决这个问题，本文提出了使用与患者无关的身份向量（i-vectors）中的信息来适应与患者无关的深度神经网络（DNN）。适应网络，即适应i-vector的患者特定DNN（iAP-DNN），针对各个患者的ECG特征进行了调整。对于每个患者，使用因子分析模型将其心电图波形压缩为i向量。然后，将此i-vector注入到独立于患者的DNN的中间隐藏层中。然后应用随机梯度下降法对整个网络进行微调，以形成针对患者的分类器。结果，该自适应不仅利用了来自特定患者的原始ECG波形，而且还利用了通过i-vector对其患者的ECG特征的紧凑表示。对隐藏层激活的分析表明，与仅使用患者专用ECG进行自适应的网络相比，通过利用i向量中的信息，iAP-DNN能够区分正常心律和心律不齐。基于MIT-BIH数据库的实验结果表明，就各种性能指标而言，iAP-DNN的性能优于现有的针对患者的分类器。特别是，