Changes in Forcecardiography Heartbeat Morphology Induced by Cardio-Respiratory Interactions

Abstract

The cardiac function is influenced by respiration. In particular, various parameters such as cardiac time intervals and the stroke volume are modulated by respiratory activity. It has long been recognized that cardio-respiratory interactions modify the morphology of cardio-mechanical signals, e.g., phonocardiogram, seismocardiogram (SCG), and ballistocardiogram. Forcecardiography (FCG) records the weak forces induced on the chest wall by the mechanical activity of the heart and lungs and relies on specific force sensors that are capable of monitoring respiration, infrasonic cardiac vibrations, and heart sounds, all simultaneously from a single site on the chest. This study addressed the changes in FCG heartbeat morphology caused by respiration. Two respiratory-modulated parameters were considered, namely the left ventricular ejection time (LVET) and a morphological similarity index (MSi) between heartbeats. The time trends of these parameters were extracted from FCG signals and further analyzed to evaluate their consistency within the respiratory cycle in order to assess their relationship with the breathing activity. The respiratory acts were localized in the time trends of the LVET and MSi and compared with a reference respiratory signal by computing the sensitivity and positive predictive value (PPV). In addition, the agreement between the inter-breath intervals estimated from the LVET and MSi and those estimated from the reference respiratory signal was assessed via linear regression and Bland–Altman analyses. The results of this study clearly showed a tight relationship between the respiratory activity and the considered respiratory-modulated parameters. Both the LVET and MSi exhibited cyclic time trends that remarkably matched the reference respiratory signal. In addition, they achieved a very high sensitivity and PPV (LVET: 94.7% and 95.7%, respectively; MSi: 99.3% and 95.3%, respectively). The linear regression analysis reported almost unit slopes for both the LVET (R2 = 0.86) and MSi (R2 = 0.97); the Bland–Altman analysis reported a non-significant bias for both the LVET and MSi as well as limits of agreement of ±1.68 s and ±0.771 s, respectively. In summary, the results obtained were substantially in line with previous findings on SCG signals, adding to the evidence that FCG and SCG signals share a similar information content.

Accurate Localization of First and Second Heart Sounds via Template Matching in Forcecardiography Signals

“…The wide bandwidth of FCG sensors allows them to monitor respiration [48], infrasonic cardiac vibrations [46,49], and heart sounds, all simultaneously from a single contact point on the chest [47]. This capability supports the accurate estimation of inter-breath and inter-beat intervals [46][47][48], as well as cardiac time intervals, such as the pre-ejection period and the left ventricular ejection time [51,52]. The infrasonic cardiac vibrations captured using FCG can be divided in two components: a low-frequency component related to emptying and filling of heart chambers, and a high-frequency component, related to the opening and closure of heart valves, which also exhibits a very high similarity with accelerometric Seismocardiography (SCG) signals [46,47].…”

“…The infrasonic cardiac vibrations captured using FCG can be divided in two components: a low-frequency component related to emptying and filling of heart chambers, and a high-frequency component, related to the opening and closure of heart valves, which also exhibits a very high similarity with accelerometric Seismocardiography (SCG) signals [ 46 , 47 ]. The infrasonic FCG components have also been shown to be affected by respiration, which causes both amplitude modulations and morphology variations [ 48 , 52 , 53 ]. In [ 47 ], a morphological comparison was carried out that confirmed the high similarity between PCG signals and the audible component of FCG signals, both in terms of morphology and acoustic impression.…”

ECG-Free Heartbeat Detection in Seismocardiography Signals via Template Matching

“…Specifically, the availability of lightweight, miniaturized, low-cost accelerometers, manufactured via micro-electromechanical systems (MEMS) technologies, has led SCG to gain particular attention, especially for the development of wearable devices. In recent years, novel, non-invasive cardio-mechanical monitoring techniques have been introduced, namely Gyrocardiography (GCG) [ 47 , 48 ], Kinocardiography (KCG) [ 49 , 50 ], and Forcecardiography (FCG) [ 51 , 52 , 53 , 54 , 55 , 56 , 57 ]. GCG records the three-dimensional angular velocities of the precordium via gyroscopes, which are often integrated into inertial measurement units (IMUs) that also contain accelerometers.…”

Heartbeat Detection in Gyrocardiography Signals without Concurrent ECG Tracings

“…Recently, novel, non-invasive, cardio-mechanical monitoring techniques have been introduced, namely Forcecardiography (FCG) [40][41][42][43][44][45][46] and Gyrocardiography (GCG) [47][48][49][50], and even the combination of SCG with BCG [51] and GCG [52,53] has been investigated. FCG captures precordial vibrations using broadband force sensors, which are able to monitor respiration, infrasonic cardiac vibrations, and heart sounds, all simultaneously from a single site on the chest.…”