Heart rate variability (HRV) biofeedback after stroke improves self-regulation of autonomic nervous system during cognitive stress

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Abstract

Objective: This study aims to investigate the self-regulation of the autonomic nervous system following the cognitive stress tests after Heart Rate Variability (HRV) biofeedback therapy in post-stroke depression (PSD) patients. Methods: Twenty-four patients with PSD were randomly divided into feedback and control groups. Feedback patients were given HRV biofeedback therapy, while the control patients only received relaxation therapy without feedback signal. HRV parameters were tracked during the cognitive stress test in quiet baseline state, cognitive stress state, and resting state, to compare the therapeutic effects of the two groups, before and after treatment. Results: Under the stress conditions, LF of both groups increased, but there were significant differences in the increasing rate (P = 0.02): LF of the feedback group increased slowly, while that of the control group increased rapidly. HRs of both groups increased during the second cognitive test, with HR increasing slowly in the feedback group and faster in the control group (P = 0.05). After rest, the HR of the feedback group decreased significantly faster than that of the control group (P = 0.05). HF of the both group increased during the stress test but showed no significant difference. Conclusion: In this paper, we show that during the cognitive stress test, patients that have received HRV biofeedback therapy can achieve a dynamic balance between sympathetic and parasympathetic nerves by reducing sympathetic sensibility, which improved patients’ adaptive capacity to cope with their internal physiological environment and external environmental pressures.

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Li, X., Sang, D., Zhang, Y., Zhang, B., & Xing, C. (2017). Heart rate variability (HRV) biofeedback after stroke improves self-regulation of autonomic nervous system during cognitive stress. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10219 LNCS, pp. 192–204). Springer Verlag. https://doi.org/10.1007/978-3-319-59858-1_19

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