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Benefits From Different Modes of Slow and Deep Breathing on Vagal Modulation.
Ma, Deshan; Li, Conghui; Shi, Wenbin; Fan, Yong; Liang, Hong; Li, Lixuan; Zhang, Zhengbo; Yeh, Chien-Hung.
Afiliação
  • Ma D; School of Information and ElectronicsBeijing Institute of Technology Beijing 100811 China.
  • Li C; Department of Child Rehabilitation MedicineThe Fifth Affiliated Hospital of Zhengzhou University Zhengzhou Henan 450052 China.
  • Shi W; School of Information and ElectronicsBeijing Institute of Technology Beijing 100811 China.
  • Fan Y; Key Laboratory of Brain Health Intelligent Evaluation and InterventionMinistry of Education (Beijing Institute of Technology) Beijing 100811 China.
  • Liang H; Centre for Artificial Intelligence in MedicineMedical Innovation Research DepartmentChinese PLA General Hospital Beijing 100036 China.
  • Li L; Centre for Artificial Intelligence in MedicineMedical Innovation Research DepartmentChinese PLA General Hospital Beijing 100036 China.
  • Zhang Z; Centre for Artificial Intelligence in MedicineMedical Innovation Research DepartmentChinese PLA General Hospital Beijing 100036 China.
  • Yeh CH; Centre for Artificial Intelligence in MedicineMedical Innovation Research DepartmentChinese PLA General Hospital Beijing 100036 China.
IEEE J Transl Eng Health Med ; 12: 520-532, 2024.
Article em En | MEDLINE | ID: mdl-39050620
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 IE ratio of 11/ 12, and intelligent guidance mode (IE ratio of 12 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 11 IE 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 IE ratio of 11 performed best during the training phase, while intelligent guidance mode had a more long-lasting effect.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Nervo Vago / Exercícios Respiratórios / Frequência Cardíaca Limite: Adult / Female / Humans / Male Idioma: En Revista: IEEE J Transl Eng Health Med Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Nervo Vago / Exercícios Respiratórios / Frequência Cardíaca Limite: Adult / Female / Humans / Male Idioma: En Revista: IEEE J Transl Eng Health Med Ano de publicação: 2024 Tipo de documento: Article