mm-Wave Radar-Based Vital Signs Monitoring and Arrhythmia Detection Using Machine Learning

Figure 1. Schematic of the proposed mm-wave radar system: an mm-wave radar is fixed at front of the subject while an Omron sphygmomanometer is attached, which simultaneously extracts pulse readings for verification. The radar data are recorded on a PC through a USB connection.

Figure 2. Transmitted and received chirp signals where each chirp is a sinusoidal signal with a slope S and time delay between transmitted and received chirp τ when the sweeping bandwidth is Sτ.

Figure 3. A MIMO radar with two transmitting antennas (Tx) and four receiving antennas (Rx) where distance between two receiving antennas is dm and θ is the angle of arrival.

Figure 4. Signal processing workflow: after applying the range FFT, for a single range bin, the angle FFTs are computed, upon which the phase shift is used to extract the heartbeat and breathing waves.

Figure 5. Symmetric triangular wave function to model the QRS components of an ECG signal.

Figure 6. Predict the onset of arrhythmia based on statistical features extracted from the phase signals of a localized range bin using an FMCW radar.

Figure 7. ECG signal pre-processing (left to right): ECG signals are extracted using electrodes mounted on the body. Hence, some artifacts are filtered out before statistical features can be extracted from the ECG signal database. Artifacts include muscle tremor, electromagnetic interference (EMI) and base-line wander. Muscle tremor artifacts caused due to sudden body movements are high-frequency signals (30~300 Hz) that are removed by Butterworth low-pass filters. The 50 Hz electromagnetic interference is suppressed by a Butterworth band-stop filter. Baseline wander is an ultra-low frequency signal that ranges between 0 and 0.8 Hz that can be eliminated using a high-pass filter. Finally, the resultant R peaks of a QRS complex are detected, and only RR interval-based features are extracted since the radar-generated heartbeat phase signals are QRS equivalent signals.

Figure 8. A confusion matrix that summarizes the model performance with true positivity rate and true negativity rate, with accuracy of 100%, 90% and 93.9%, respectively.

Figure 9. The mean squared error during training reduced to a very low value at the 9th epoch. However, with early stopping enabled, the best performance was obtained at the 3rd epoch when the validation MSE = 0.025.

Figure 10. Gradient optimization using gradient descent and momentum.

Figure 11. Periodogram of input heartbeat phase signal.

Figure 12. Signal reconstruction using symmetric triangular wave function, which was then down sampled to 5 Hz to match the sampling frequency of the ECG signals used in the training dataset.