Heart Rate Estimation from Ballistocardiography Based on Hilbert Transform and Phase Vocoder

Xie, Qingsong; Wang, Guoxing; Lian, Yong

doi:10.1109/apccas.2018.8605724

Cited by 10 publications

(6 citation statements)

References 16 publications

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“…The error was slightly smaller compared with that in our study (2.4 bpm). In addition, Xie et al [20] reported a 0.90 bpm mean absolute error in HR detection by a BCG sensor placed under a chair during 15-minute recordings and a greater correlation (r=0.98) compared with the present study. In previous studies, the participants were instructed to avoid movement since muscular activity may cause measurement errors in BCG.…”

Section: Comparison With Prior Workcontrasting

confidence: 62%

A Contact-Free, Ballistocardiography-Based Monitoring System (Emfit QS) for Measuring Nocturnal Heart Rate and Heart Rate Variability: Validation Study

Ville¹

2020

JMIR Biomed Eng

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Background Heart rate (HR) and heart rate variability (HRV) measurements are widely used to monitor stress and recovery status in sedentary people and athletes. However, effective HRV monitoring should occur on a daily basis because sparse measurements do not allow for a complete view of the stress-recovery balance. Morning electrocardiography (ECG) measurements with HR straps are time-consuming and arduous to perform every day, and thus compliance with regular measurements is poor. Contact-free, ballistocardiography (BCG)-based Emfit QS is effortless for daily monitoring. However, to the best of our knowledge, there is no study on the accuracy of nocturnal HR and HRV measured via BCG under real-life conditions. Objective The aim of this study was to evaluate the accuracy of Emfit QS in measuring nocturnal HR and HRV. Methods Healthy participants (n=20) completed nocturnal HR and HRV recordings at home using Emfit QS and an ECG-based reference device (Firstbeat BG2) during sleep. Emfit QS measures BCG by a ferroelectret sensor installed under a bed mattress. HR and the root mean square of successive differences between RR intervals (RMSSD) were determined for 3-minute epochs and the sleep period mean. Results A trivial mean bias was observed in the mean HR (mean –0.8 bpm [beats per minute], SD 2.3 bpm, P=.15) and Ln (natural logarithm) RMSSD (mean –0.05 ms, SD 0.25 ms, P=.33) between Emfit QS and ECG. In addition, very large correlations were found in the mean values of HR (r=0.90, P<.001) and Ln RMSSD (r=0.89, P<.001) between the devices. A greater amount of erroneous or missing data (P<.001) was observed in the Emfit QS measurements (28.3%, SD 14.4%) compared with the reference device (1.1%, SD 2.3%). The results showed that 5.0% of the mean HR and Ln RMSSD values were outside the limits of agreement. Conclusions Based on the present results, Emfit QS provides nocturnal HR and HRV data with an acceptable, small mean bias when calculating the mean of the sleep period. Thus, Emfit QS has the potential to be used for the long-term monitoring of nocturnal HR and HRV. However, further research is needed to assess reliability in HR and HRV detection.

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Section: Comparison With Prior Workcontrasting

confidence: 62%

A Contact-Free, Ballistocardiography-Based Monitoring System (Emfit QS) for Measuring Nocturnal Heart Rate and Heart Rate Variability: Validation Study

Ville¹

2020

JMIR Biomed Eng

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“…An IMF is charaterised by a cosine function with a time-dependent frequency and amplitude. The BCG signal can be assumed to consist of mainly two intrinsic mode functions [22,23], in form of an amplitude modulation, i.e. an IMF associated with the heart rate superposed by one associated with the respiratory rate, i.e.…”

Section: Methodsmentioning

confidence: 99%

“…HR and RR refer to the IMF for the heart rate (HR) and the respiratory rate (RR). Similar to [22,23], to separate the respiratory IMF from the one associated with the heart, a Butterworth bandpass filter with cut-off frequencies at 0.1 Hz and 0.5 Hz , according to respiration rates of 6 and 30 breaths per minute (BPM), can be used. The residual signal can thus be assumed to consist only of the IMF for the respiration as well as additive filtered noise and motion artefacts Applying the Hilbert transform to this IMF then leads to the so-called analytic component where i denotes the imaginary unit.…”

Section: Methodsmentioning

confidence: 99%

“…Deviating from other approaches using the Hilbert transform for extracting the heart rate, as in, e.g. [22,23], the phase signal of the Hilbert transform is used instead of the amplitude signal. The approach is similar to the one used in Empirical Mode Decomposition for estimating the instantaneous frequency in multi-component signals which was first postulated by Huang et al [24].…”

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confidence: 99%

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Estimation of the respiratory rate from ballistocardiograms using the Hilbert transform

et al. 2022

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Background Measuring the respiratory rate is usually associated with discomfort for the patient due to contact sensors or a high time demand for healthcare personnel manually counting it. Methods In this paper, two methods for the continuous extraction of the respiratory rate from unobtrusive ballistocardiography signals are introduced. The Hilbert transform is used to generate an amplitude-invariant phase signal in-line with the respiratory rate. The respiratory rate can then be estimated, first, by using a simple peak detection, and second, by differentiation. Results By analysis of a sleep laboratory data set consisting of nine records of healthy individuals lasting more than 63 h and including more than 59,000 breaths, a mean absolute error of as low as 0.7 BPM for both methods was achieved. Conclusion The results encourage further assessment for hospitalised patients and for home-care applications especially with patients suffering from diseases of the respiratory system like COPD or sleep apnoea.

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“…Formally, the Hilbert transform is the response to g(t) of a linear time-invariant filter having impulse response 1/πt, this filter is called Hilbert Transformer, which is the convolution of g(t) with the signal 1/πt. The Equation 9 express the definition of the Hilbert Transform [30], [6].…”

Section: Hilbert Transformmentioning

confidence: 99%

Comparison of Ballistocardiogram Processing Methods Based on Fiber Specklegram Sensors

2022

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The ballistocardiogram is a graphic representation of the movements of the body associated with cardiac activity. In this paper, a ten minutes ballistocardiogram have been captured for ten different volunteers with a polymer optical fiber (POF) specklegram sensor. This transducer, which is composed by a CCD camera, a laser emitting diode and two meters of POF, allows to capture the ballistocardiogram by analyzing how the induced speckle pattern changes over the time. These changes are related to cardiac activity. Several processing methods have been compared to determine which method achieve the best performance: Complex Cepstrum, Power of Spectral Density (PSD), Pam-Tompkins algorithm, Wavelet, Autocorrelation, Savitzky-Golay filter, Mean Absolute Deviation and Hilbert transform. Accuracy and resources consumption have been characterized and compared for these methods. Hilbert, PSD and Savitzky-golay exhibit both small error and computational cost. This paper describes a baseline for main frequency determination of POF-based ballistocardiogram signals in real time.

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Heart Rate Estimation from Ballistocardiography Based on Hilbert Transform and Phase Vocoder

Cited by 10 publications

References 16 publications

A Contact-Free, Ballistocardiography-Based Monitoring System (Emfit QS) for Measuring Nocturnal Heart Rate and Heart Rate Variability: Validation Study

A Contact-Free, Ballistocardiography-Based Monitoring System (Emfit QS) for Measuring Nocturnal Heart Rate and Heart Rate Variability: Validation Study

Estimation of the respiratory rate from ballistocardiograms using the Hilbert transform

Comparison of Ballistocardiogram Processing Methods Based on Fiber Specklegram Sensors

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