Abdollahpur Mostafa, Engström Gunnar, Platonov Pyotr G, Sandberg Frida
Department of Biomedical Engineering, Lund University, Lund, Sweden.
Department of Clinical Sciences, Cardiovascular Research-Epidemiology, Malmö, Sweden.
Front Physiol. 2022 Sep 19;13:976925. doi: 10.3389/fphys.2022.976925. eCollection 2022.
The autonomic nervous system (ANS) is known as a potent modulator of the initiation and perpetuation of atrial fibrillation (AF), hence information about ANS activity during AF may improve treatment strategy. Respiratory induced ANS variation in the f-waves of the ECG may provide such information. This paper proposes a novel approach for improved estimation of such respiratory induced variations and investigates the impact of deep breathing on the f-wave frequency in AF patients. A harmonic model is fitted to the f-wave signal to estimate a high-resolution f-wave frequency trend, and an orthogonal subspace projection approach is employed to quantify variations in the frequency trend that are linearly related to respiration using an ECG-derived respiration signal. The performance of the proposed approach is evaluated and compared to that of a previously proposed bandpass filtering approach using simulated f-wave signals. Further, the proposed approach is applied to analyze ECG data recorded for 5 min during baseline and 1 min deep breathing from 28 AF patients from the Swedish cardiopulmonary bioimage study (SCAPIS). The simulation results show that the estimates of respiratory variations obtained using the proposed approach are more accurate than estimates obtained using the previous approach. Results from the analysis of SCAPIS data show no significant differences between baseline and deep breathing in heart rate (75.5 ± 22.9 vs. 74 ± 22.3) bpm, atrial fibrillation rate (6.93 ± 1.18 vs. 6.94 ± 0.66) Hz and respiratory f-wave frequency variations (0.130 ± 0.042 vs. 0.130 ± 0.034) Hz. However, individual variations are large with changes in heart rate and atrial fibrillatory rate in response to deep breathing ranging from -9% to +5% and -8% to +6%, respectively and there is a weak correlation between changes in heart rate and changes in atrial fibrillatory rate ( = 0.38, < 0.03). Respiratory induced f-wave frequency variations were observed at baseline and during deep breathing. No significant changes in the magnitude of these variations in response to deep breathing was observed in the present study population.
自主神经系统（ANS）是已知的心房颤动（AF）起始和持续的强效调节因子，因此关于AF期间ANS活动的信息可能会改善治疗策略。心电图f波中呼吸诱导的ANS变化可能提供此类信息。本文提出了一种改进此类呼吸诱导变化估计的新方法，并研究了深呼吸对AF患者f波频率的影响。将谐波模型拟合到f波信号以估计高分辨率的f波频率趋势，并采用正交子空间投影方法，使用心电图衍生的呼吸信号来量化与呼吸线性相关的频率趋势变化。使用模拟f波信号评估所提出方法的性能，并与先前提出的带通滤波方法进行比较。此外，将所提出的方法应用于分析瑞典心肺生物图像研究（SCAPIS）中28名AF患者在基线期记录5分钟以及深呼吸1分钟期间的心电图数据。模拟结果表明，使用所提出方法获得的呼吸变化估计比使用先前方法获得的估计更准确。对SCAPIS数据的分析结果表明，心率（75.5±22.9对74±22.3）bpm、心房颤动率（6.93±1.18对6.94±0.66）Hz和呼吸f波频率变化（0.130±0.042对0.130±0.034）Hz在基线期和深呼吸之间无显著差异。然而，个体差异很大，深呼吸时心率和心房颤动率的变化分别为-9%至+5%和-8%至+6%，且心率变化与心房颤动率变化之间存在弱相关性（r = 0.38，P < 0.03）。在基线期和深呼吸期间均观察到呼吸诱导的f波频率变化。在本研究人群中，未观察到这些变化幅度在深呼吸时的显著变化。
