Improving Psychological Resilience to Acute Stress and Anxiety: A Non-Invasive Solution T o Attain Sympathovagal Balance Using Novel Neuro-Cardiac Biomarkers

Overview

Chronic stress and anxiety often cause mental and physical dysfunctions. Inevitable cardiac responses usually accompany as a part of the autonomic nervous system’s reaction to combat stress. But frequent autonomic imbalances have been strongly implicated in the pathophysiology of depression, post-traumatic stress disorder (PTSD), and other mental illnesses. However, the central questions about stress and its connection to heart- brain mechanisms are poorly understood. Physical touch has long proven to improve HRV (heart rate variability) by engaging our parasympathetic system to reduce stress by releasing serotonin, dopamine, and oxytocin. The current study investigates a non-invasive solution to quantify stress, attain autonomic nervous system balance, and improve cardiovascular health. Simultaneous electrocardiogram (ECG) and electroencephalogram (EEG) recordings were obtained from eighteen subjects while administering stress-inducing Stroop Color-Word Test (SCWT) and Paced Auditory Serial Addition Test (PASAT). A random forest supervised machine learning classification model is developed to quantify these recordings and strongly correlate brain fatigue and HRV. A low-cost wearable device is built to run the ML model to predict the incoming stress signals and generate preprogrammed vibrational waves (89Hz - 114Hz). These waves can induce a sense of soothing touch, which naturally engages the parasympathetic system lowering heart rate and improving HRV. Experimental results showed that the wearable device predicted the incoming stress signal at an average accuracy of 98.54% with an average inference time of 2.1s to restore HRV. This research provides evidence that we can non-invasively improve HRV and build strong resilience to stress and anxiety by attaining autonomic nervous system balance.