PDF(Pubmed)
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.
摘要：
缓慢深呼吸（SDB）是一种可以增加迷走神经活动的放松技术。呼吸性窦性心律失常（RSA）是迷走神经功能的指标，通常由心率变异性（HRV）的高频功率量化。然而,SDB期间的低呼吸率导致通过HRV估计RSA时的偏差。此外,吸气-呼气(I:E)比率和指导方式(固定呼吸频率或智能指导)对SDB的影响尚不清楚.在我们的研究中,30名健康人（平均年龄=26.5岁，17名女性）参加了三种SDB模式，包括每分钟6次呼吸(bpm)，I:E比为1:1/1:2，以及智能引导模式(I:E比为1:2，引导逐渐降低呼吸频率至6bpm)。从HRV导出的参数，多模态耦合分析(MMCA)，庞加莱情节，引入去趋势波动分析来检验SDB演习的效果。此外,应用多种机器学习方法对呼吸模式进行分类(自主呼吸与SDB）在通过最大相关性和最小冗余进行特征选择之后。所有迷走神经活动标记,尤其是MMCA衍生的RSA，在SDB期间统计增加。在所有SDB模式中，以1:1I:E比例呼吸6bpm时，迷走神经功能在统计学上最活跃，虽然智能制导模式有更多的指标，但训练后仍然有显著增加，包括SDRR和MMCA衍生的RSA，等。关于呼吸模式的分类，朴素贝叶斯分类器具有最高的准确率(92.2%)，输入特征包括LFn，C百分比，pNN50，[公式：见正文]，SDRatio,[公式:见正文],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SDB识别和实时反馈训练效果。我们证明，在训练阶段，I：E比为1：1的6bpm呼吸表现最佳。而智能制导模式具有更持久的效果。
