An algorithm for beat-to-beat heart rate detection from the BCG based on the continuous spline wavelet transform

Alvarado-Serrano, Carlos; Luna-Lozano, Pablo S.; Pallás-Areny, R.

doi:10.1016/j.bspc.2016.02.002

Cited by 42 publications

(23 citation statements)

References 30 publications

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“…The proposed approach is applied to six healthy subjects standing on a bathroom scale equipped with multiple strain gages. A similar approach is proposed in [8], where authors used CWT with splines for optimal scale selection. However, this approach required a training phase for parameter initialization.…”

Section: Related Workmentioning

confidence: 99%

Continuous and unconstrained vital signs monitoring with ballistocardiogram sensors in headrest position

Sadek

Biswas

Abdulrazak³

et al. 2017

2017 IEEE EMBS International Conference on Biomedical &Amp; Health Informatics (BHI)

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Unobtrusive and long-term monitoring of human vital signs are essential requirements for early diagnosis and prophylaxis due to many reasons, one of the most important being improving the quality of life. Currently, vital signs are continuously monitored through sensors attached to the body, such as multiple electrodes for measuring electrical activity of the heart. Such methods may be undesirable, especially for elderly, infants and other groups of people. In this paper, we introduce an improved technique for measuring heart rate from noisy ballistocardiogram signals acquired from 50 human volunteers in a sitting position using a massage chair. The signals are unobtrusively collected from a microbend fiber optic sensor embedded within the headrest of the chair, and then transmitted to a computer through a Bluetooth connection. The heart rate is computed using the multiresolution analysis of the maximal overlap discrete wavelet transform. The error between the proposed method and the reference ECG is estimated in beats per minute using the mean absolute error, where the system achieved relatively good results (7.31 ± 1.60) despite the large amount of motion artifacts produced owing to the frequent body movements and/or vibrations of the massage chair during stress relief massage. Unlike the complete ensemble empirical mode decomposition algorithm, previously employed for heart rate estimation, the suggested system is much faster. Hence, it can be used in real-time applications.

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Section: Related Workmentioning

confidence: 99%

Continuous and unconstrained vital signs monitoring with ballistocardiogram sensors in headrest position

Sadek

Biswas

Abdulrazak³

et al. 2017

2017 IEEE EMBS International Conference on Biomedical &Amp; Health Informatics (BHI)

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“…In particular, for single beds, a MAE of 13.22 ms is achieved (at a sample frequency of 300 Hz, i.e., T s = 3.33 ms); however, no statistically significant difference is found between those factors. On the other hand, reference [40] reports a standard deviation σ e = 14.35 ms; a similar setup, featuring a commercial, accelerometer-based solution [37], achieved −0.5 ± 1.6 beats per minute (mean ± standard deviation, i.e. −8.3 ± 26.7 ms) in HR estimation accuracy.…”

Section: Overall Performance Discussionmentioning

confidence: 99%

Accurate Heartbeat Detection on Ballistocardiogram Accelerometric Traces

Mora

Cocconcelli

Matrella

et al. 2020

IEEE Trans. Instrum. Meas.

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This paper presents an automated procedure for acquisition and analysis of BallistoCardioGraphy (BCG) traces. A tri-axial accelerometer and a microcontroller unit are used to record heart-induced recoil forces generated from a lying subject. The problem of BCG J-peak annotation is split into two sub-tasks: candidates extraction, based on a detection signal, and actual annotation, guided by subject-specific search windows. Such procedure is derived from an automatic calibration, which is carried out with no need of concurrent ElectroCardioGram (ECG) or user intervention. The algorithm also implements post-annotation checks for refinement of annotation, which effectively reduces the number of missed J-peaks. The impact of each algorithm phase is analyzed, assessing statistical significance of each step; finally, performance is optimized in a data-driven fashion. Results show that the proposed methodology is able to achieve high sensitivity and precision (the median score is 98.9% and 98.1%, respectively) in J-peak detection. The quality of J-peaks timing annotation is further demonstrated by a very low discrepancy between BCG and ECG HR estimates. Over all population, the standard deviation of such error was found to be approximately 6.56 ms, whereas the Mean Absolute Error just 4.7 ms (i.e. ≈ 1.18; Ts, where Ts = 4 ms is the sampling period). Such scores, indeed, improve over recent related literature.

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“…Alvarado-Serrano et al [25] measured beat-to-beat heart rate from subjects sitting in a common office chair. The authors used a piezoelectric sensor fixed to the bottom side of the seat to collect ballistocardiogram signals from seven subjects (5 males and 2 females).…”

Section: Piezoelectric Polyvinylidene Fluoride-based Sensorsmentioning

confidence: 99%

Ballistocardiogram signal processing: a review

Sadek

Biswas

Abdulrazak

2019

Health Inf Sci Syst

126

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There are several algorithms for analyzing and interpreting cardiorespiratory signals obtained from in-bed based sensors. In sum, these algorithms can be broadly grouped into three categories: time-domain algorithms, frequency-domain algorithms, and wavelet-domain algorithms. A summary of these algorithms is given below to highlight which category of algorithms will be used in our analysis. First, time-domain algorithms are mainly focused on detecting local maxima or local minima using a moving window, and therefore finding the interval between the dominant J-peaks of ballistocardiogram signal. However, this approach has many limitations because of the nonlinear and nonstationary behavior of the ballistocardiogram signal. The implication is that the ballistocardiogram signal does not display consistent J-peaks, which can usually be the case for overnight, in-home monitoring, particularly with frail elderly. Additionally, its accuracy will be undoubtedly affected by motion artifacts. Second, frequency-domain algorithms do not provide information about interbeat intervals. Nevertheless, they can provide information about heart rate variability. This is usually done by taking the fast Fourier transform or the inverse Fourier transform of the logarithm of the estimated spectrum, i.e., cepstrum of the signal using a sliding window. Thereafter, the dominant frequency is obtained in a particular frequency range. The limit of these algorithms is that the peak in the spectrum may get wider and multiple peaks may appear, which might cause a problem in measuring the vital signs. At last, the objective of wavelet-domain algorithms is to decompose the signal into different components, hence the component which shows an agreement with the vital signs can be selected. In other words, the selected component contains only information about the heart cycles or respiratory cycles, respectively. Interbeat intervals can be found easily by applying a simple peak detector. An empirical mode decomposition is an alternative approach to wavelet decomposition, and it is also a very suitable approach to cope with nonlinear and nonstationary signals such as cardiorespiratory signals. Apart from the above-mentioned algorithms, machine learning approaches have been implemented for measuring heartbeats. However, manual labeling of training data is a restricting property. Furthermore, the training step should be repeated whenever the data collection protocol has been changed.

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An algorithm for beat-to-beat heart rate detection from the BCG based on the continuous spline wavelet transform

Cited by 42 publications

References 30 publications

Continuous and unconstrained vital signs monitoring with ballistocardiogram sensors in headrest position

Continuous and unconstrained vital signs monitoring with ballistocardiogram sensors in headrest position

Accurate Heartbeat Detection on Ballistocardiogram Accelerometric Traces

Ballistocardiogram signal processing: a review

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