Determination of the Effects of Transcutaneous Auricular Vagus Nerve Stimulation on the Heart Rate Variability Using a Machine Learning Pipeline.

Background: We are all aware of day-to-day healthy stress, but, when sustained for long periods, stress is believed to lead to serious physical and mental health issues.

Materials and methods: In this study, we investigated the potential effects of transcutaneous auricular vagus nerve stimulation (taVNS) on stress processing as reflected in the electrocardiogram (ECG)-derived biomarkers of stress adaptability. Stress reflecting biomarkers included a range of heart rate variability metrics: standard deviation of N-N intervals (SDNN), root mean squared of successive differences in heartbeat intervals (RMSSD), low-frequency component, high-frequency component and their ratio (LF, HF, and LF/HF).In addition, we created a machine learning model capable of distinguishing between the stimulated and nonstimulated conditions from the ECG-derive data from various subjects and states. The model consisted of a deep convolutional neural network, which was trained on R-R interval (RRI) data extracted from ECG and time traces of LF, HF, LF/HF, SDNN, and RMSSD.

Results: Only LF/HF ratio demonstrated a statistically significant change in response to stimulation. Although the LF/HF ratio is expected to increase during exposure to stress, we have observed that stimulation during exposure to stress counteracts this increase or even reduces the LF/HF ratio. This could be an indication that the vagus nerve stimulation decreases the sympathetic activation during stress inducement.Our Machine Learning model achieved an accuracy of 70% with no significant variations across the three states (baseline, stress, and recovery). However, training an analogous neural network to identify the states (baseline, stress, and recovery) proved to be unsuccessful.

Conclusion: Overall, in this study, we showed further evidence of the beneficial effect of taVNS on stress processing. Importantly we have also demonstrated the promising potential of ECG metrics as a biomarker for the development of closed-loop stimulation systems.