Using a non-invasive multi-sensor device to evaluate left atrial pressure: an estimated filling pressure derived from ballistocardiography

Zhang, Li; Cai, Peiwei; Deng, Yinlong; Lin, Jiumin; Wu, Muli; Xiao, Zhongbo; Chu, Zhengpei; Shi, Qingfeng; Ye, Fei; Hu, Junhao; Yang, Caiqin; Li, Pengyang; Zhuang, Shaochun; Wang, Bin

doi:10.21037/atm-21-5161

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…More recently, these techniques have been used in domains such as atrial fibrillation ( 10 ) and hypertension detection ( 11 ), heart failure monitoring ( 12 , 13 ), cardiorespiratory fitness assessment ( 14 , 15 ), and many others. Recent advances in the field of SCG were published by Taebi et al ( 16 ).…”

Section: Introductionmentioning

confidence: 99%

Non-invasive cardiac kinetic energy distribution: a new marker of heart failure with impaired ejection fraction (KINO-HF)

Keyzer

Hossein

Rabineau

et al. 2023

Front. Cardiovasc. Med.

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BackgroundHeart failure (HF) remains a major cause of mortality, morbidity, and poor quality of life. 44% of HF patients present impaired left ventricular ejection fraction (LVEF). Kinocardiography (KCG) technology combines ballistocardiography (BCG) and seismocardiography (SCG). It estimates myocardial contraction and blood flow through the cardiac chambers and major vessels through a wearable device. Kino-HF sought to evaluate the potential of KCG to distinguish HF patients with impaired LVEF from a control group.MethodsSuccessive patients with HF and impaired LVEF (iLVEF group) were matched and compared to patients with normal LVEF ≥ 50% (control). A 60 s KCG acquisition followed cardiac ultrasound. The kinetic energy from KCG signals was computed in different phases of the cardiac cycle (iKsystolic;ΔiKdiastolic) as markers of cardiac mechanical function.ResultsThirty HF patients (67 [59; 71] years, 87% male) were matched with 30 controls (64.5 [49; 73] years, 87% male). SCG ΔiKdiastolic, BCG iKsystolic, BCG ΔiKdiastolic were lower in HF than controls (p < 0.05), while SCG iKsystolic was similar. Furthermore, a lower SCG iKsystolic was associated with an increased mortality risk during follow-up.ConclusionsKINO-HF demonstrates that KCG can distinguish HF patients with impaired systolic function from a control group. These favorable results warrant further research on the diagnostic and prognostic capabilities of KCG in HF with impaired LVEF.Clinical Trial Registration: NCT03157115.

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Section: Introductionmentioning

confidence: 99%

Non-invasive cardiac kinetic energy distribution: a new marker of heart failure with impaired ejection fraction (KINO-HF)

Keyzer

Hossein

Rabineau

et al. 2023

Front. Cardiovasc. Med.

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“…In this study, 12 BCG features were recorded using a modified-weighing scale in addition to 6 extracted features from ECG signals. Elevated left atrial pressure (LAP) is considered as a HF indicator in this work ( Zhang et al, 2021 ), they collected thoracic vibration signals through the optical BCG device that is fixed on each patient’s back, then verified the left atrial pressure index with a 0.81 overall agreement rate as a derived parameter for heart failure detection.…”

Section: Non-invasive Sensing Technologies Used For Heart Failure Sys...mentioning

confidence: 99%

Non-invasive technologies for heart failure, systolic and diastolic dysfunction modeling: a scoping review

Al Younis,

Hadjileontiadis,

Stefanini

et al. 2023

Front. Bioeng. Biotechnol.

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The growing global prevalence of heart failure (HF) necessitates innovative methods for early diagnosis and classification of myocardial dysfunction. In recent decades, non-invasive sensor-based technologies have significantly advanced cardiac care. These technologies ease research, aid in early detection, confirm hemodynamic parameters, and support clinical decision-making for assessing myocardial performance. This discussion explores validated enhancements, challenges, and future trends in heart failure and dysfunction modeling, all grounded in the use of non-invasive sensing technologies. This synthesis of methodologies addresses real-world complexities and predicts transformative shifts in cardiac assessment. A comprehensive search was performed across five databases, including PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar, to find articles published between 2009 and March 2023. The aim was to identify research projects displaying excellence in quality assessment of their proposed methodologies, achieved through a comparative criteria-based rating approach. The intention was to pinpoint distinctive features that differentiate these projects from others with comparable objectives. The techniques identified for the diagnosis, classification, and characterization of heart failure, systolic and diastolic dysfunction encompass two primary categories. The first involves indirect interaction with the patient, such as ballistocardiogram (BCG), impedance cardiography (ICG), photoplethysmography (PPG), and electrocardiogram (ECG). These methods translate or convey the effects of myocardial activity. The second category comprises non-contact sensing setups like cardiac simulators based on imaging tools, where the manifestations of myocardial performance propagate through a medium. Contemporary non-invasive sensor-based methodologies are primarily tailored for home, remote, and continuous monitoring of myocardial performance. These techniques leverage machine learning approaches, proving encouraging outcomes. Evaluation of algorithms is centered on how clinical endpoints are selected, showing promising progress in assessing these approaches’ efficacy.

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“…The same effectiveness was also proved during the COVID-19 pandemic ( 51 ). Different categories of BCG instrumentation have been used by the scientific community while trying to address in particular the problem of continuous and unobtrusive cardiac remote monitoring ( 52 , 53 ). BCG signal also proved to be comparable with both PPG ( 54 , 55 ) and ECG ( 56 , 57 ) signals to have a ground truth value while checking HR estimation performances.…”

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

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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.

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Using a non-invasive multi-sensor device to evaluate left atrial pressure: an estimated filling pressure derived from ballistocardiography

Cited by 4 publications

References 34 publications

Non-invasive cardiac kinetic energy distribution: a new marker of heart failure with impaired ejection fraction (KINO-HF)

Non-invasive cardiac kinetic energy distribution: a new marker of heart failure with impaired ejection fraction (KINO-HF)

Non-invasive technologies for heart failure, systolic and diastolic dysfunction modeling: a scoping review

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

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