Unobtrusive Inter-beat Interval Estimation from Multichannel Ballistocardiogram Signal Using Kalman Filter

Huang, Yudong; Sun, C. P.; Jin, Tianchen; Yang, Shuchen; Zhang, Zhiming

doi:10.1109/embc44109.2020.9176247

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…which is validated with (10). If it is rejected, we perform the last search B5 > 70 ms: B5 > 150 ms finding 1 0= which is verified through (11).…”

Section: ) Decision Stagementioning

confidence: 95%

“…Therefore, several automatic algorithms for J-wave and thus HR detection have been proposed, most of them focusing on single-position, singleaxis BCG recordings, also because few public databases exist. Techniques vary from algorithms based on the signal envelope [10], filters [11], Continuous Wavelet Transform (CWT) [12], [13], Discrete Wavelet Transform (DWT) [14], Multiresolution Analysis of the Maximal Overlap Discrete Wavelet Transform (MODWT-MRA) [15] and adaptive beat shape modeling [16]. Methods based on wavelet analysis have proven to be successful regardless of the type of sensors or experimental setups [17], in addition to being effective for processing nonlinear and non-stationary physiological signals [18], [19].…”

Section: Introductionmentioning

confidence: 99%

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J Wave Detection Algorithm of the BCG in Chair and Bed using Continuous Spline Wavelet Transform

Flores-Nunez

Garcia-Limon

Martínez-Suárez

et al. 2022

2022 19th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

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This study presents an algorithm for unsupervised beat-to-beat detection of the J-wave of the ballistocardiogram (BCG) in records of both lying (bed) and seated (chair) persons. The algorithm is based on the continuous wavelet transform (CWT) with splines, which offers the advantage of using a wide range of scales and the reduction of noise and mechanical interference. For J-wave detection, the most prominent negative modulus of the CWT is detected using adaptive time windows (the negative modulus provides more information about the location of the J-wave), and then a confirmation is performed from temporal and amplitude parameters. Seven records from a chair database and fifteen records from a bed database were used to evaluate the algorithm. To assess the Jwave detection, the Bland Altman test was used, measuring the heart rate (HR) from the ECG as a reference and considering a 95% confidence interval (±2 SD). For the bed database the mean error was -0.03 beats/min with a confidence interval of ±3.87 and for the chair database the mean error was -0.05 beats/min with a confidence interval of ±3.48 beats/min. Results satisfied the standards for HR meters recommended by the Association for the Advancement of Medical Instrumentation (AAMI).

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“…which is validated with (10). If it is rejected, we perform the last search B5 > 70 ms: B5 > 150 ms finding 1 0= which is verified through (11).…”

Section: ) Decision Stagementioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

J Wave Detection Algorithm of the BCG in Chair and Bed using Continuous Spline Wavelet Transform

Flores-Nunez

Garcia-Limon

Martínez-Suárez

et al. 2022

2022 19th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

View full text Add to dashboard Cite

show abstract

“…In addition, BCG is a nonstationary signal. This means that, although the HR evaluation from BCG in the frequency domain does not rely on the presence of specific peaks or templates within the waveform, in a fixed BCG signal window, a prominent frequency component may result anyway, missing most of the time ( 23 , 24 ). To overcome such a shortcoming, a number of related peak detection and/or heartbeat interval estimation methods are proposed in the literature.…”

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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Novel algorithm for beat-to-beat heart rate measurement from the BCG in seated, standing and supine positions: Towards an universal algorithm

García-Limón,

Flores-Nuñez,

Alvarado-Serrano

et al. 2024

Biomedical Signal Processing and Control

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Unobtrusive Inter-beat Interval Estimation from Multichannel Ballistocardiogram Signal Using Kalman Filter

Cited by 8 publications

References 22 publications

J Wave Detection Algorithm of the BCG in Chair and Bed using Continuous Spline Wavelet Transform

J Wave Detection Algorithm of the BCG in Chair and Bed using Continuous Spline Wavelet Transform

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

Novel algorithm for beat-to-beat heart rate measurement from the BCG in seated, standing and supine positions: Towards an universal algorithm

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