Benefits From Different Modes of Slow and Deep Breathing on Vagal Modulation

IEEE J Transl Eng Health Med. 2024 Jun 27:12:520-532. doi: 10.1109/JTEHM.2024.3419805. eCollection 2024.

Abstract

Slow and deep breathing (SDB) is a relaxation technique that can increase vagal activity. Respiratory sinus arrhythmia (RSA) serves as an index of vagal function usually quantified by the high-frequency power of heart rate variability (HRV). However, the low breathing rate during SDB results in deviations when estimating RSA by HRV. Besides, the impact of the inspiration-expiration (I: E) ratio and guidelines ways (fixed breathing rate or intelligent guidance) on SDB is not yet clear. In our study, 30 healthy people (mean age = 26.5 years, 17 females) participated in three SDB modes, including 6 breaths per minute (bpm) with an I:E ratio of 1:1/ 1:2, and intelligent guidance mode (I:E ratio of 1:2 with guiding to gradually lower breathing rate to 6 bpm). Parameters derived from HRV, multimodal coupling analysis (MMCA), Poincaré plot, and detrended fluctuation analysis were introduced to examine the effects of SDB exercises. Besides, multiple machine learning methods were applied to classify breathing patterns (spontaneous breathing vs. SDB) after feature selection by max-relevance and min-redundancy. All vagal-activity markers, especially MMCA-derived RSA, statistically increased during SDB. Among all SDB modes, breathing at 6 bpm with a 1:1 I:E ratio activated the vagal function the most statistically, while the intelligent guidance mode had more indicators that still significantly increased after training, including SDRR and MMCA-derived RSA, etc. About the classification of breathing patterns, the Naive Bayes classifier has the highest accuracy (92.2%) with input features including LFn, CPercent, pNN50, [Formula: see text], SDRatio, [Formula: see text], and LF. Our study proposed a system that can be applied to medical devices for automatic SDB identification and real-time feedback on the training effect. We demonstrated that breathing at 6 bpm with an I:E ratio of 1:1 performed best during the training phase, while intelligent guidance mode had a more long-lasting effect.

Keywords: Slow deep breathing; detrended fluctuation analysis (DFA); heart rate variability (HRV); inspiration-expiration ratio; multimodal coupling analysis (MMCA).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Breathing Exercises* / methods
  • Electrocardiography
  • Female
  • Heart Rate* / physiology
  • Humans
  • Machine Learning
  • Male
  • Respiration
  • Respiratory Rate / physiology
  • Respiratory Sinus Arrhythmia / physiology
  • Signal Processing, Computer-Assisted
  • Vagus Nerve* / physiology
  • Young Adult

Grants and funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62171028, Grant 62001026, and Grant 62171471; in part by Beijing Natural Science Foundation under Grant L232139; in part by the Open Project of Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province under Grant MEDH202204 and Grant MEDC202303; and in part by the High-Level Fellow Research Fund Program under Grant 3050012222022.