A frequency domain analysis of respiratory variations in the seismocardiogram signal

Pandia, Keya; Inan, Omer T.; Kovacs, G.T.A.

doi:10.1109/embc.2013.6611139

Cited by 19 publications

(8 citation statements)

References 7 publications

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“…6f shows typical data (top) and the time-frequency signal (bottom) captured at a sampling rate of 1600 Hz from a representative neonate. The signals associated with crying have distinctive frequencies (typically between 400 and 500 Hz, with strong harmonics), well separated from other physiological effects such as cardiac activity (1 -50 Hz) and muscle tremors (< 20 Hz) [43][44][45] or from various operations in care such as patting, rubbing or stroking (see Supplementary Figs. 24a-c).…”

Section: Advanced Use Cases: Kangaroo Care and Cry Analysismentioning

confidence: 99%

Skin-interfaced biosensors for advanced wireless physiological monitoring in neonatal and pediatric intensive-care units

Chung

Rwei

Hourlier-Fargette

et al. 2020

Nat Med

313

193

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Standard of care management in neonatal and pediatric intensive care units (NICUs and PICUs) involve continuous monitoring of vital signs with hard-wired devices that adhere to the skin and, in certain instances, include catheter-loaded pressure sensors that insert into the arteries. These protocols involve risks for complications and impediments to clinical care and skin-to-skin contact between parent and child. Here we present a wireless, non-invasive technology that not only offers measurement equivalency to these management standards but also supports a range of important additional features (without limitations or shortcomings of existing approaches), supported by data from pilot clinical studies in the neonatal intensive care unit (NICU) and pediatric ICU (PICU). The combined capabilities of these platforms extend beyond clinical quality measurements of vital signs (heart rate, respiration rate, temperature and blood oxygenation) to include novel modalities for (1) tracking movements and changes in body orientation, (2) quantifying the physiological benefits of skin-to-skin care (e.g. Kangaroo care) for neonates, (3) capturing acoustic signatures of cardiac activity by directly measuring mechanical vibrations generated through the skin on the chest, (4) recording vocal biomarkers associated with tonality and temporal characteristics of crying impervious to confounding ambient noise, and (5) monitoring a reliable surrogate for systolic blood pressure. The results have potential to significantly enhance the quality of neonatal and pediatric critical care.In the United States, over 480,000 critically-ill infants and children enter intensive care units (ICUs) each year. Those less than one year of age suffer from the highest morbidity and mortality rates and therefore require the most intensive care 1,2 . These fragile patients include

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Section: Advanced Use Cases: Kangaroo Care and Cry Analysismentioning

confidence: 99%

Skin-interfaced biosensors for advanced wireless physiological monitoring in neonatal and pediatric intensive-care units

Chung

Rwei

Hourlier-Fargette

et al. 2020

Nat Med

313

193

View full text Add to dashboard Cite

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“…A respiration belt was also used to measure the respiratory information, and it was close to SCG-based information. In [126], cardiac vibrations were used to find cardiopulmonary health. Respiration effect was investigated on the frequency domain of the SCG signal.…”

Section: Respiratory Informationmentioning

confidence: 99%

A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications

et al. 2021

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In recent years, cardiovascular diseases are on the rise, and they entail enormous health burdens on global economies. Cardiac vibrations yield a wide and rich spectrum of essential information regarding the functioning of the heart, and thus it is necessary to take advantage of this data to better monitor cardiac health by way of prevention in early stages. Specifically, seismocardiography (SCG) is a noninvasive technique that can record cardiac vibrations by using new cutting-edge devices as accelerometers. Therefore, providing new and reliable data regarding advancements in the field of SCG, i.e., new devices and tools, is necessary to outperform the current understanding of the State-of-the-Art (SoTA). This paper reviews the SoTA on SCG and concentrates on three critical aspects of the SCG approach, i.e., on the acquisition, annotation, and its current applications. Moreover, this comprehensive overview also presents a detailed summary of recent advancements in SCG, such as the adoption of new techniques based on the artificial intelligence field, e.g., machine learning, deep learning, artificial neural networks, and fuzzy logic. Finally, a discussion on the open issues and future investigations regarding the topic is included.

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“…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%

Changes in Forcecardiography Heartbeat Morphology Induced by Cardio-Respiratory Interactions

Centracchio

Esposito

Andreozzi

2022

Sensors

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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.

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A frequency domain analysis of respiratory variations in the seismocardiogram signal

Cited by 19 publications

References 7 publications

Skin-interfaced biosensors for advanced wireless physiological monitoring in neonatal and pediatric intensive-care units

Skin-interfaced biosensors for advanced wireless physiological monitoring in neonatal and pediatric intensive-care units

A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications

Changes in Forcecardiography Heartbeat Morphology Induced by Cardio-Respiratory Interactions

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