Author: Deshan Ma1, Conghui Li2, Wenbin Shi1,3, Yong Fan4, Hong Liang4, Lixuan Li4, Zhengbo Zhang4, Chien-Hung Yeh1,3
Affiliation: <sup>1</sup> School of Information and ElectronicsBeijing Institute of Technology Beijing 100811 China.
<sup>2</sup> Department of Child Rehabilitation MedicineThe Fifth Affiliated Hospital of Zhengzhou University Zhengzhou Henan 450052 China.
<sup>3</sup> Key Laboratory of Brain Health Intelligent Evaluation and InterventionMinistry of Education (Beijing Institute of Technology) Beijing 100811 China.
<sup>4</sup> Centre for Artificial Intelligence in MedicineMedical Innovation Research DepartmentChinese PLA General Hospital Beijing 100036 China.
Conference/Journal: IEEE J Transl Eng Health Med
Date published: 2024 Jun 27
Other:
Volume ID: 12 , Pages: 520-532 , Special Notes: doi: 10.1109/JTEHM.2024.3419805. , Word Count: 314
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).
PMID: 39050620 PMCID: PMC11268930 DOI: 10.1109/JTEHM.2024.3419805