  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
