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

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL IEEE Journal of Translational Engineering in Health and Medicine-Jtehm Pub Date : 2024-06-27 DOI:10.1109/JTEHM.2024.3419805
Deshan Ma;Conghui Li;Wenbin Shi;Yong Fan;Hong Liang;Lixuan Li;Zhengbo Zhang;Chien-Hung Yeh
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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, $\alpha 2$ , SDRatio, $\alpha 1$ , 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.
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不同模式的慢速深呼吸对迷走神经调节的益处
慢而深的呼吸(SDB)是一种可以增加迷走神经活动的放松技术。呼吸窦性心律失常(RSA)是迷走神经功能的一个指标,通常通过心率变异性(HRV)的高频功率进行量化。然而,SDB 期间的低呼吸频率会导致用心率变异估计 RSA 时出现偏差。此外,吸呼比(I:E)和指导方式(固定呼吸频率或智能指导)对 SDB 的影响也尚未明确。在我们的研究中,30 名健康人(平均年龄 = 26.5 岁,17 名女性)参与了三种 SDB 模式,包括 I:E 比为 1:1/ 1:2 的每分钟 6 次呼吸(bpm)和智能引导模式(I:E 比为 1:2,引导呼吸频率逐渐降低至 6 bpm)。研究人员引入了心率变异、多模态耦合分析(MMCA)、Poincaré图和去趋势波动分析等参数来检验 SDB 运动的效果。此外,在通过最大相关性和最小冗余度选择特征后,多种机器学习方法被用于对呼吸模式(自主呼吸与 SDB)进行分类。所有迷走神经活动标记物,尤其是MMCA衍生的RSA,在SDB期间均有统计学意义的增加。在所有SDB模式中,以1:1的I:E比例进行6 bpm的呼吸在统计学上最能激活迷走神经功能,而智能引导模式有更多的指标在训练后仍显著增加,包括SDRR和MMCA衍生RSA等。关于呼吸模式的分类,Naive Bayes 分类器的准确率最高(92.2%),输入特征包括 LFn、CPercent、pNN50、$\alpha 2$ 、SDRatio、$\alpha 1$ 和 LF。我们的研究提出了一种可应用于医疗设备的系统,用于自动识别 SDB 并实时反馈训练效果。我们证明,在训练阶段,呼吸频率为 6 bpm、I:E 比为 1:1 的呼吸效果最好,而智能引导模式的效果更持久。
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来源期刊
CiteScore
7.40
自引率
2.90%
发文量
65
审稿时长
27 weeks
期刊介绍: The IEEE Journal of Translational Engineering in Health and Medicine is an open access product that bridges the engineering and clinical worlds, focusing on detailed descriptions of advanced technical solutions to a clinical need along with clinical results and healthcare relevance. The journal provides a platform for state-of-the-art technology directions in the interdisciplinary field of biomedical engineering, embracing engineering, life sciences and medicine. A unique aspect of the journal is its ability to foster a collaboration between physicians and engineers for presenting broad and compelling real world technological and engineering solutions that can be implemented in the interest of improving quality of patient care and treatment outcomes, thereby reducing costs and improving efficiency. The journal provides an active forum for clinical research and relevant state-of the-art technology for members of all the IEEE societies that have an interest in biomedical engineering as well as reaching out directly to physicians and the medical community through the American Medical Association (AMA) and other clinical societies. The scope of the journal includes, but is not limited, to topics on: Medical devices, healthcare delivery systems, global healthcare initiatives, and ICT based services; Technological relevance to healthcare cost reduction; Technology affecting healthcare management, decision-making, and policy; Advanced technical work that is applied to solving specific clinical needs.
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