Estimation of the respiratory rate from ballistocardiograms using the Hilbert transform

Linschmann, Onno; Leonhardt, Steffen; Vehkaoja, Antti; Antink, Christoph Hoog

doi:10.1186/s12938-022-01024-4

Cited by 5 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The retrospective analysis presented in [ 83 ] showed that respiration causes amplitude modulations of the dHF-FCG component. This finding was in agreement with the existing literature on respiratory-induced changes in SCG signals [ 71 , 73 , 74 , 75 , 76 ] and provided further evidence of the close matching between the information content of dHF-FCG and SCG signals.…”

Section: Introductionsupporting

confidence: 92%

“…It has long been recognized that cardio-mechanical signals are affected by various concurrent physiological activities, including respiration [ 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 ]. Several studies have addressed the respiratory-induced variations of heartbeat morphology in SCG, aiming either to improve the performance of ensemble averaging by applying it separately to groups of similar heartbeats or to extract respiratory signals to be used for the monitoring of the breathing activity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Changes in Forcecardiography Heartbeat Morphology Induced by Cardio-Respiratory Interactions

Centracchio

Esposito

Andreozzi

2022

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Changes in Forcecardiography Heartbeat Morphology Induced by Cardio-Respiratory Interactions

Centracchio

Esposito

Andreozzi

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Issues emerged in cases with motion artifacts caused by body movement. Instead, following the original empirical mode decomposition approach postulated for the first time by Huang et al ( 30 ) to estimate the instantaneous frequency in multi-component signals, Linschmann et al ( 31 ) proposed two methods based on selective filtering and the Hilbert transform for obtaining the respiration from BCG signals.…”

Section: Related Workmentioning

confidence: 99%

Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation

Tramontano,

Tamburis,

Cioce

et al. 2023

Front. Digit. Health

View full text Add to dashboard Cite

Medical devices (MDs) have been designed for monitoring the parameters of patients in many sectors. Nonetheless, despite being high-performing and reliable, they often turn out to be expensive and intrusive. In addition, MDs are almost exclusively used in controlled, hospital-based environments. Paving a path of technological innovation in the clinical field, a very active line of research is currently dealing with the possibility to rely on non-medical-graded low-cost devices, to develop unattended telemedicine (TM) solutions aimed at non-invasively gathering data, signals, and images. In this article, a TM solution is proposed for monitoring the heart rate (HR) of patients during sleep. A remote patient monitoring system (RPMS) featuring a smart belt equipped with pressure sensors for ballistocardiogram (BCG) signals sampling was deployed. A field trial was then conducted over a 2-month period on 24 volunteers, who also agreed to wear a finger pulse oximeter capable of producing a photoplethysmography (PPG) signal as the gold standard, to examine the feasibility of the solution via the estimation of HR values from the collected BCG signals. For this purpose, two of the highest-performing approaches for HR estimation from BCG signals, one algorithmic and the other based on a convolutional neural network (CNN), were retrieved from the literature and updated for a TM-related use case. Finally, HR estimation performances were assessed in terms of patient-wise mean absolute error (MAE). Results retrieved from the literature (controlled environment) outperformed those achieved in the experimentation (TM environment) by 29% (MAE = 4.24 vs. 5.46, algorithmic approach) and 52% (MAE = 2.32 vs. 3.54, CNN-based approach), respectively. Nonetheless, a low packet loss ratio, restrained elaboration time of the collected biomedical big data, low-cost deployment, and positive feedback from the users, demonstrate the robustness, reliability, and applicability of the proposed TM solution. In light of this, further steps will be planned to fulfill new targets, such as evaluation of respiratory rate (RR), and pattern assessment of the movement of the participants overnight.

show abstract

Blood pressure monitoring with piezoelectric bed sensor systems

Xing,

Li,

Chen

et al. 2024

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

Estimation of the respiratory rate from ballistocardiograms using the Hilbert transform

Cited by 5 publications

References 31 publications

Changes in Forcecardiography Heartbeat Morphology Induced by Cardio-Respiratory Interactions

Changes in Forcecardiography Heartbeat Morphology Induced by Cardio-Respiratory Interactions

Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation

Blood pressure monitoring with piezoelectric bed sensor systems

Contact Info

Product

Resources

About