Biomedical Acoustic Research Lab, University of Central Florida, Orlando, FL, USA.
Mechanical Power Engineering Department, Zagazig University, Zagazig, Egypt.
Sci Rep. 2024 Aug 2;14(1):17904. doi: 10.1038/s41598-024-68590-6.
Seismocardiographic (SCG) signals are chest wall vibrations induced by cardiac activity and are potentially useful for cardiac monitoring and diagnosis. SCG waveform is observed to vary with respiration, but the mechanism of these changes is poorly understood as alterations in autonomic tone, lung volume, heart location and intrathoracic pressure are all varying during the respiratory cycle. Understanding SCG variability and its sources may help reduce variability and increase SCG clinical utility. This study investigated SCG variability during breath holding (BH) at two different lung volumes (i.e., end inspiration and end expiration) and five airway pressures (i.e., 0, ± 2-4, and ± 15-20 cm HO). Variability during normal breathing was also studied with and without grouping SCG beats into two clusters of similar waveform morphologies (performed using the K-medoid algorithm in an unsupervised machine learning fashion). The study included 15 healthy subjects (11 Females and 4 males, Age: 21 ± 2 y) where SCG, ECG, and spirometry were simultaneously acquired. SCG waveform variability was calculated at each experimental state (i.e., lung volume and airway pressure). Results showed that breath holding was more effective in reducing the intra-state variability of SCG than clustering normal breathing data. For the BH states, the intra-state variability increased as the airway pressure deviated from zero. The subaudible-to-audible energy ratio of the BH states increased as the airway pressure decreased below zero which may be related to the effect of the intrathoracic pressure on cardiac afterload and blood ejection. When combining the BH waveforms at end inspiration and end expiration states (at the same airway pressures) into one group, the intra-state variability increased, which suggests that the lung volume and associated change in heart location were a significant source of variability. The linear trend between airway pressure and waveform changes was found to be statistically significant for BH at end expiration. To confirm these findings, more studies are needed with a larger number of airway pressure levels and larger number of subjects.
心震图（SCG）信号是由心脏活动引起的胸壁振动，对于心脏监测和诊断具有潜在的应用价值。观察到 SCG 波形随呼吸而变化，但这些变化的机制尚不清楚，因为在呼吸周期中自主神经张力、肺容积、心脏位置和胸腔内压力都在变化。了解 SCG 的可变性及其来源可能有助于减少可变性并提高 SCG 的临床实用性。本研究在两种不同肺容积（即吸气末和呼气末）和五种气道压力（即 0、±2-4 和 ±15-20 cmH2O）下研究了呼吸暂停期间的 SCG 变异性。还研究了在正常呼吸时不分组和分组 SCG 搏动为两个具有相似波形形态的簇（使用无监督机器学习中的 K-medoid 算法执行）时的 SCG 变异性。该研究包括 15 名健康受试者（11 名女性和 4 名男性，年龄 21 ± 2 岁），同时采集了 SCG、心电图和肺活量计数据。在每个实验状态（即肺容积和气道压力）下计算 SCG 波形的变异性。结果表明，与聚类正常呼吸数据相比，呼吸暂停在降低 SCG 内状态变异性方面更有效。对于 BH 状态，随着气道压力偏离零，内状态变异性增加。BH 状态下的次声至可闻声能量比随着气道压力的降低而增加，这可能与胸腔内压力对心脏后负荷和血液射血的影响有关。当将吸气末和呼气末状态（在相同的气道压力下）的 BH 波形组合成一组时，内状态变异性增加，这表明肺容积和相关的心脏位置变化是变异性的重要来源。发现 BH 在呼气末时气道压力和波形变化之间存在线性趋势，具有统计学意义。为了证实这些发现，需要进行更多的研究，包括更多的气道压力水平和更多的受试者。
