Centracchio Jessica, Andreozzi Emilio, Esposito Daniele, Gargiulo Gaetano D
Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio, 80125 Napoli, Italy.
School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia.
Bioengineering (Basel). 2022 Sep 7;9(9):444. doi: 10.3390/bioengineering9090444.
Forcecardiography (FCG) is a novel technique that records the weak forces induced on the chest wall by cardio-respiratory activity, by using specific force sensors. FCG sensors feature a wide frequency band, which allows us to capture respiration, heart wall motion, heart valves opening and closing (similar to the Seismocardiogram, SCG) and heart sounds, all simultaneously from a single contact point on the chest. As a result, the raw FCG sensors signals exhibit a large component related to the respiratory activity, referred to as a Forcerespirogram (FRG), with a much smaller, superimposed component related to the cardiac activity (the actual FCG) that contains both infrasonic vibrations, referred to as LF-FCG and HF-FCG, and heart sounds. Although respiration can be readily monitored by extracting the very low-frequency component of the raw FCG signal (FRG), it has been observed that the respiratory activity also influences other FCG components, particularly causing amplitude modulations (AM). This preliminary study aimed to assess the consistency of the amplitude modulations of the LF-FCG and HF-FCG signals within the respiratory cycle. A retrospective analysis was performed on the FCG signals acquired in a previous study on six healthy subjects at rest, during quiet breathing. To this aim, the AM of LF-FCG and HF-FCG were first extracted via a linear envelope (LE) operation, consisting of rectification followed by low-pass filtering; then, the inspiratory peaks were located both in the LE of LF-FCG and HF-FCG, and in the reference respiratory signal (FRG). Finally, the inter-breath intervals were extracted from the obtained inspiratory peaks, and further analyzed via statistical analyses. The AM of HF-FCG exhibited higher consistency within the respiratory cycle, as compared to the LF-FCG. Indeed, the inspiratory peaks were recognized with a sensitivity and positive predictive value (PPV) in excess of 99% in the LE of HF-FCG, and with a sensitivity and PPV of 96.7% and 92.6%, respectively, in the LE of LF-FCG. In addition, the inter-breath intervals estimated from the HF-FCG scored a higher R value (0.95 vs. 0.86) and lower limits of agreement (± 0.710 s vs. ±1.34 s) as compared to LF-FCG, by considering those extracted from the FRG as the reference. The obtained results are consistent with those observed in previous studies on SCG. A possible explanation of these results was discussed. However, the preliminary results obtained in this study must be confirmed on a larger cohort of subjects and in different experimental conditions.
力心动图（FCG）是一种新技术，它通过使用特定的力传感器来记录心肺活动在胸壁上诱发的微弱力。FCG传感器具有很宽的频带，这使我们能够从胸部的单个接触点同时捕捉呼吸、心脏壁运动、心脏瓣膜的开闭（类似于心震图，SCG）以及心音。因此，FCG传感器的原始信号呈现出一个与呼吸活动相关的大成分，称为力呼吸图（FRG），以及一个与心脏活动相关的小得多的叠加成分（实际的FCG），其中包含次声振动，称为低频FCG和高频FCG，还有心音。虽然通过提取原始FCG信号（FRG）的极低频成分可以很容易地监测呼吸，但据观察，呼吸活动也会影响其他FCG成分，特别是会引起幅度调制（AM）。这项初步研究旨在评估呼吸周期内低频FCG和高频FCG信号幅度调制的一致性。对之前一项针对六名健康受试者在静息状态下安静呼吸时采集的FCG信号进行了回顾性分析。为此，首先通过线性包络（LE）操作提取低频FCG和高频FCG的AM，该操作包括整流后进行低通滤波；然后，在低频FCG和高频FCG的LE以及参考呼吸信号（FRG）中定位吸气峰值。最后，从获得的吸气峰值中提取呼吸间隔，并通过统计分析进一步分析。与低频FCG相比，高频FCG的AM在呼吸周期内表现出更高的一致性。事实上，在高频FCG的LE中识别吸气峰值的灵敏度和阳性预测值（PPV）超过99%，而在低频FCG的LE中，灵敏度和PPV分别为96.7%和92.6%。此外，以从FRG中提取的呼吸间隔为参考，与低频FCG相比，从高频FCG估计得到的呼吸间隔的R值更高（0.95对0.86），一致性界限更低（±0.710秒对±1.34秒）。所得结果与之前关于SCG的研究中观察到的结果一致。讨论了这些结果的一种可能解释。然而，本研究获得的初步结果必须在更大的受试者队列和不同的实验条件下得到证实。
