Using wearable sensors to study the brain-heart interactions during attentional and resting states

Wearable sensors have a significant increase in both research and commercialization as a kind of consumer electronics. In the field of healthy science, wearable sensors provide affordable solutions for massive screening or long tern monitoring, for instance, wearable dry electroencephalography (EEG) for brain and electrocardiogram (ECG) for heart. Human brain and heart are the most important organs and the targets for healthy monitoring. Given that brain and heart sometimes have comorbidities of each other as they are reciprocally connected, it is important to monitor their relations. However, only a few studies addressed the relationships between them under healthy states. This study aims to examine the brain-heart interactions(BHI) under different states, including two different resting states and one attentional state using wearable sensors. Twenty subject were recruited and performed a memory task in a virtual supermarket. Five-channel dry EEG and two-channel ECG were acquired before, during, and after the task. Pearson correlation was employed to analyze the relations between EEG features and heart rate variability (HRV). Two sample t test and machine learning method were employed to analysis the difference between states. We found that higher frequency oscillations in the brain were used to communicate with the heart during the pre-rest state and task state respectively, then switched to lower frequency for resetting the BHI after task. Moreover, the combination of EEG and ECG features can best distinguish the pre and post resting states. Our findings suggest that BHI are dynamic and state-dependent and studying the BHI can provide better understanding of the states in the body to aid the diagnosis and/or treatment for diseases affected both brain and heart.