A Bayesian model of heart rate to reveal real-time physiological information

Quer, Giorgio; Rao, Ramesh R.

doi:10.1109/healthcom.2012.6379412

Cited by 5 publications

(5 citation statements)

References 17 publications

(19 reference statements)

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“…Since a heartbeat is a periodic continuous-time signal, Quer [30] proved that the Laplacian model could be used to analyze the heart rate data for predicting the next peak of Figure 3 shows a PPG signal, which is further applied to form a signal vector for heart rate detection. Observe that the signal vector in the moving window consists of overlapping and non-overlapping peaks.…”

Section: Laplacian Distributionmentioning

confidence: 99%

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An Adaptive Heart Rate Monitoring Algorithm for Wearable Healthcare Devices

Kuo

Teng

et al. 2021

Electronics

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This paper focuses on developing an adaptive heart rate monitoring algorithm for wrist-based rehabilitation systems. Due to the characteristics of the wrist, the heartbeat measurements are unstable. To improve the preprocessing efficiency and perform measurement calibration, a novel joint algorithm incorporating automatic multiscale-based peak detection and fuzzy logic control (AMPD-Fuzzy) is proposed. The monitoring approach consists of two phases: (1) Preprocessing and (2) Detection and Calibration. Phase 1 explores the parameter settings, threshold, and decision rules. Phase 2 applies fuzzy logic control and the Laplacian model to provide signal reshaping. Experimental results show that the proposed algorithm can effectively achieve heart rate monitoring for wearable healthcare devices.

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Section: Laplacian Distributionmentioning

confidence: 99%

“…Since a heartbeat is a periodic continuous-time signal, Quer [30] proved that the Laplacian model could be used to analyze the heart rate data for predicting the next peak of the signal. Denote t m as the mth instant of time corresponding to a heartbeat.…”

Section: Laplacian Distributionmentioning

confidence: 99%

An Adaptive Heart Rate Monitoring Algorithm for Wearable Healthcare Devices

Kuo

Teng

et al. 2021

Electronics

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“…An enhancement could be achieved by calculating the probability of having a peak at sample i by incorporating historical previous knowledge about R-peaks. In [10], the authors show that the Laplacian model exhibits the best fit for analysing the d process.…”

Section: Ampd Output Modification and Prior Probabilistic Knowledgementioning

confidence: 99%

“…Substituting (10) and (12) into (9), we get the posterior probability distribution of θ i as a new Beta distribution B(α′, β′), i.e.…”

Section: Ampd Output Modification and Prior Probabilistic Knowledgementioning

confidence: 99%

“…

p_{1}^{false(i false)} = \frac{\sum_{k = 1}^{L} m_{k, i}}{L}

An enhancement could be achieved by calculating the probability of having a peak at sample i by incorporating historical previous knowledge about R‐peaks. In [10], the authors show that the Laplacian model exhibits the best fit for analysing the d process. Based on their results, the probability p 2 of sample i to be a peak using Laplacian distribution is given by

p_{2}^{false(i false)} = \frac{1}{2 b} \exp (- \frac{| d_{i} - μ |}{b})

Since the decision is not yet made whether sample i is a peak, d i definition in (6) slightly differs from the general one in (4).…”

Section: Algorithm Descriptionmentioning

confidence: 99%

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Heart rate variability estimation in photoplethysmography signals using Bayesian learning approach

Alqaraawi

Alwosheel

Alasaad

2016

Healthcare Technology Letters

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Heart rate variability (HRV) has become a marker for various health and disease conditions. Photoplethysmography (PPG) sensors integrated in wearable devices such as smart watches and phones are widely used to measure heart activities. HRV requires accurate estimation of time interval between consecutive peaks in the PPG signal. However, PPG signal is very sensitive to motion artefact which may lead to poor HRV estimation if false peaks are detected. In this Letter, the authors propose a probabilistic approach based on Bayesian learning to better estimate HRV from PPG signal recorded by wearable devices and enhance the performance of the automatic multi scale-based peak detection (AMPD) algorithm used for peak detection. The authors’ experiments show that their approach enhances the performance of the AMPD algorithm in terms of number of HRV related metrics such as sensitivity, positive predictive value, and average temporal resolution.

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