论文标题
基于CNN的多面信号处理框架，用于使用毫米波雷达弹药摄影
A CNN based Multifaceted Signal Processing Framework for Heart Rate Proctoring Using Millimeter Wave Radar Ballistocardiography
论文作者
论文摘要
最近的大流行使医学界重新关注与心血管疾病相关的诊断技术。心率提供了心血管健康的实时快照。更精确的心率阅读可以更好地了解心肌活动。尽管许多现有的诊断技术正在接近完美的局限性，但仍有进一步发展的潜力。在本文中，我们提出了Mibinet，这是一种通过毫米波（MM-WAVE）雷达ballistarcartiography信号从毫米间间隔（IBI）实时降低心率的卷积神经网络。该网络由于其轻巧且无接触式的特性，可用于医院，房屋和乘用车。在将数据拟合到网络中之前，它采用经典信号处理。尽管Mibinet主要设计用于在MM波信号上工作，但发现它在PCG，ECG和PPG等各种模式的信号上同样有效。广泛的实验结果和与当前在MM波信号的最新面前的彻底比较证明了所提出的方法的可行性和多功能性。 关键字：心血管疾病，非接触式测量，心率，IBI，MM波雷达，神经网络
The recent pandemic has refocused the medical world's attention on the diagnostic techniques associated with cardiovascular disease. Heart rate provides a real-time snapshot of cardiovascular health. A more precise heart rate reading provides a better understanding of cardiac muscle activity. Although many existing diagnostic techniques are approaching the limits of perfection, there remains potential for further development. In this paper, we propose MIBINET, a convolutional neural network for real-time proctoring of heart rate via inter-beat-interval (IBI) from millimeter wave (mm-wave) radar ballistocardiography signals. This network can be used in hospitals, homes, and passenger vehicles due to its lightweight and contactless properties. It employs classical signal processing prior to fitting the data into the network. Although MIBINET is primarily designed to work on mm-wave signals, it is found equally effective on signals of various modalities such as PCG, ECG, and PPG. Extensive experimental results and a thorough comparison with the current state-of-the-art on mm-wave signals demonstrate the viability and versatility of the proposed methodology. Keywords: Cardiovascular disease, contactless measurement, heart rate, IBI, mm-wave radar, neural network
