Millimetre wave radar is an emerging technology that can monitor vital signs without contact. This unique feature is very suitable for some particular situations, such as burn patient monitoring. Currently, electrocardiogram (ECG) is still the most common approach for monitoring heart disease. Deep learning algorithms have already been applied to classifying ECG recordings and have achieved good diagnostic results. However, it is very rare to see deep learning-based heartbeat classification using radar signals. The reason is a lack of radar-based heart disease datasets, which are the most important part of training a Convolutional Neural Network (CNN). Specifically, the ECG recordings and radar signals are heterogeneous; thus, the ECG dataset cannot train the CNN for directly classifying the radar signals. In this paper, we propose a novel signal processing algorithm called the Common Features Extraction Method (CFEM) to extract the common features of ECG recordings and radar signals to train a CNN for radar heartbeat signal classification. By using CFEM, the ECG dataset is transferred to the radar field, which means that the core issue for training the CNN using radar signals has been solved. Practical experiments show that the CFEM-based CNN can classify heartbeat radar signals accurately.

毫米波雷达是一种新兴技术，可以不接触地监测生命体征。此独特功能非常适合某些特定情况，例如烧伤病人监护。当前，心电图（ECG）仍然是监测心脏病的最常用方法。深度学习算法已经应用于对ECG记录进行分类，并取得了良好的诊断结果。但是，很少见到使用雷达信号进行基于深度学习的心跳分类。原因是缺乏基于雷达的心脏病数据集，这是训练卷积神经网络（CNN）的最重要部分。具体来说，心电图记录和雷达信号是异类的；因此，ECG数据集无法训练CNN以直接对雷达信号进行分类。在本文中，我们提出了一种新颖的信号处理算法，称为通用特征提取方法（CFEM），以提取ECG记录和雷达信号的通用特征，以训练CNN进行雷达心跳信号分类。通过使用CFEM，将ECG数据集传输到雷达领域，这意味着使用雷达信号训练CNN的核心问题已解决。实际实验表明，基于CFEM的CNN可以对心跳雷达信号进行准确分类。