Ballistocardiography Can Estimate Beat-to-Beat Heart Rate Accurately at Night in Patients After Vascular Intervention

Antink, Christoph Hoog; Mai, Yen; Aalto, Roosa; Brüser, Christoph; Leonhardt, Steffen; Oksala, Niku; Vehkaoja, Antti

doi:10.1109/jbhi.2020.2970298

Cited by 23 publications

(12 citation statements)

References 16 publications

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“…Therefore, research on anti-interference algorithm is the key to improve the accuracy of BCG analysis. For example, Antink et al [19] used a continuous local interval estimator (CLIE) approach to estimate the beat-to-beat intervals (BBIs). To reduce the influence of frequent motion artifacts on the analysis results, the CLIE method used ultrashort-term HRV analysis.…”

Section: Introductionmentioning

confidence: 99%

A Noncontact Ballistocardiography-Based IoMT System for Cardiopulmonary Health Monitoring of Discharged COVID-19 Patients

Liu

Miao

Yin

et al. 2021

IEEE Internet Things J.

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We developed a ballistocardiography (BCG)-based Internet of medical things (IoMT) system for remote monitoring of cardiopulmonary health. The system composes of BCG sensor, edge node, and cloud platform. To improve computational efficiency and system stability, the system adopted collaborative computing between edge nodes and cloud platforms. Edge nodes undertake signal processing tasks, namely approximate entropy for signal quality assessment, a lifting wavelet scheme for separating the BCG and respiration signal, and the lightweight BCG and respiration signal peaks detection. Heart rate variability (HRV), respiratory rate variability (RRV) analysis and other intelligent computing are performed on cloud platform. In experiments with 25 participants, the proposed method achieved a mean absolute error (MAE)±standard deviation of absolute error (SDAE) of 9.6±8.2 ms for heartbeat intervals detection, and a MAE±SDAE of 22.4±31.1 ms for respiration intervals detection. To study the recovery of cardiopulmonary function in patients with coronavirus disease 2019 (COVID-19), this study recruited 186 discharged patients with COVID-19 and 186 control volunteers. The results indicate that the recovery performance of the respiratory rhythm is better than the heart rhythm among discharged patients with COVID-19. This reminds the patients to be aware of the risk of cardiovascular disease after recovering from COVID-19. Therefore, our remote monitoring system has the ability to play a major role in the follow up and management of discharged patients with COVID-19. Index Terms-Ballistocardiography, Noncontact health monitoring, Internet of medical things, cardiopulmonary health, Discharged patients with COVID-19 I. INTRODUCTIONT HE ongoing coronavirus disease 2019 (COVID-19) pandemic has caused tens of millions of people worldwide to be infected. COVID-19 is the result of infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); this virus can cause severe acute respiratory syndrome, which can induce the generalized attenuation of excitatory pathways that regulate the respiratory and cardiovascular systems [1].

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Section: Introductionmentioning

confidence: 99%

A Noncontact Ballistocardiography-Based IoMT System for Cardiopulmonary Health Monitoring of Discharged COVID-19 Patients

Liu

Miao

Yin

et al. 2021

IEEE Internet Things J.

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“…The need for simple, low-cost and non-invasive methods to monitor the cardiovascular system has encouraged the development of PW systems as an alternative to ECG [ 14 , 15 , 44 ]. In recent years, this need has also led to the re-emergence of BCG acquisition systems [ 45 , 46 , 47 , 48 ]. However, ECG and PW systems currently require that the sensors are either applied manually to the patient’s skin or, for longer-term use, are held in position with an adhesive patch, a strap or a more elaborate mechanical device such as the one used in this study for PW measurements.…”

Section: Discussionmentioning

confidence: 99%

Statistical Analysis of the Consistency of HRV Analysis Using BCG or Pulse Wave Signals

Cui

Wang

et al. 2022

Sensors

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Ballistocardiography (BCG) is considered a good alternative to HRV analysis with its non-contact and unobtrusive acquisition characteristics. However, consensus about its validity has not yet been established. In this study, 50 healthy subjects (26.2 ± 5.5 years old, 22 females, 28 males) were invited. Comprehensive statistical analysis, including Coefficients of Variation (CV), Lin’s Concordance Correlation Coefficient (LCCC), and Bland-Altman analysis (BA ratio), were utilized to analyze the consistency of BCG and ECG signals in HRV analysis. If the methods gave different answers, the worst case was taken as the result. Measures of consistency such as Mean, SDNN, LF gave good agreement (the absolute value of CV difference < 2%, LCCC > 0.99, BA ratio < 0.1) between J-J (BCG) and R-R intervals (ECG). pNN50 showed moderate agreement (the absolute value of CV difference < 5%, LCCC > 0.95, BA ratio < 0.2), while RMSSD, HF, LF/HF indicated poor agreement (the absolute value of CV difference ≥ 5% or LCCC £ 0.95 or BA ratio ≥ 0.2). Additionally, the R-R intervals were compared with P-P intervals extracted from the pulse wave (PW). Except for pNN50, which exhibited poor agreement in this comparison, the performances of the HRV indices estimated from the PW and the BCG signals were similar.

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“…To quantify the performance of the proposed sensor mat for HR and BR estimates compared with the reference ECG signal and effort belt signal, the Bland-Altman plot, Pearson correlation coefficient, normalized root mean square error (NRMSE), normalized mean absolute error (NMAE), and mean absolute percentage error (MAPE) were adopted. These metrics are commonly employed to determine the difference between medical instruments [ 44 - 48 ]. Further information about these error metrics can be found in Multimedia Appendix 1 .…”

Section: Methodsmentioning

confidence: 99%

A New Approach for Detecting Sleep Apnea Using a Contactless Bed Sensor: Comparison Study

Sadek¹,

Heng²,

Seet³

et al. 2020

J Med Internet Res

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Background At present, there is an increased demand for accurate and personalized patient monitoring because of the various challenges facing health care systems. For instance, rising costs and lack of physicians are two serious problems affecting the patient’s care. Nonintrusive monitoring of vital signs is a potential solution to close current gaps in patient monitoring. As an example, bed-embedded ballistocardiogram (BCG) sensors can help physicians identify cardiac arrhythmia and obstructive sleep apnea (OSA) nonintrusively without interfering with the patient’s everyday activities. Detecting OSA using BCG sensors is gaining popularity among researchers because of its simple installation and accessibility, that is, their nonwearable nature. In the field of nonintrusive vital sign monitoring, a microbend fiber optic sensor (MFOS), among other sensors, has proven to be suitable. Nevertheless, few studies have examined apnea detection. Objective This study aims to assess the capabilities of an MFOS for nonintrusive vital signs and sleep apnea detection during an in-lab sleep study. Data were collected from patients with sleep apnea in the sleep laboratory at Khoo Teck Puat Hospital. Methods In total, 10 participants underwent full polysomnography (PSG), and the MFOS was placed under the patient’s mattress for BCG data collection. The apneic event detection algorithm was evaluated against the manually scored events obtained from the PSG study on a minute-by-minute basis. Furthermore, normalized mean absolute error (NMAE), normalized root mean square error (NRMSE), and mean absolute percentage error (MAPE) were employed to evaluate the sensor capabilities for vital sign detection, comprising heart rate (HR) and respiratory rate (RR). Vital signs were evaluated based on a 30-second time window, with an overlap of 15 seconds. In this study, electrocardiogram and thoracic effort signals were used as references to estimate the performance of the proposed vital sign detection algorithms. Results For the 10 patients recruited for the study, the proposed system achieved reasonable results compared with PSG for sleep apnea detection, such as an accuracy of 49.96% (SD 6.39), a sensitivity of 57.07% (SD 12.63), and a specificity of 45.26% (SD 9.51). In addition, the system achieved close results for HR and RR estimation, such as an NMAE of 5.42% (SD 0.57), an NRMSE of 6.54% (SD 0.56), and an MAPE of 5.41% (SD 0.58) for HR, whereas an NMAE of 11.42% (SD 2.62), an NRMSE of 13.85% (SD 2.78), and an MAPE of 11.60% (SD 2.84) for RR. Conclusions Overall, the recommended system produced reasonably good results for apneic event detection, considering the fact that we are using a single-channel BCG sensor. Conversely, satisfactory results were obtained for vital sign detection when compared with the PSG outcomes. These results provide preliminary support for the potential use of the MFOS for sleep apnea detection.

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Ballistocardiography Can Estimate Beat-to-Beat Heart Rate Accurately at Night in Patients After Vascular Intervention

Cited by 23 publications

References 16 publications

A Noncontact Ballistocardiography-Based IoMT System for Cardiopulmonary Health Monitoring of Discharged COVID-19 Patients

A Noncontact Ballistocardiography-Based IoMT System for Cardiopulmonary Health Monitoring of Discharged COVID-19 Patients

Statistical Analysis of the Consistency of HRV Analysis Using BCG or Pulse Wave Signals

A New Approach for Detecting Sleep Apnea Using a Contactless Bed Sensor: Comparison Study

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