Combining ensemble empirical mode decomposition with spectrum subtraction technique for heart rate monitoring using wrist-type photoplethysmography

Zhang, Yangsong; Liu, Benyuan; Zhang, Zhilin

doi:10.1016/j.bspc.2015.05.006

Cited by 83 publications

(55 citation statements)

References 18 publications

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“…(13)(14)(15)(16) To avoid inaccurate estimation caused by MAs, these studies limit the estimation range of the heart rate. The methods used, however, cannot deal with rapid heart rate changes caused by changes in exercise intensity.…”

Section: Related Workmentioning

confidence: 99%

“…(3) There are some studies focusing on the reduction of the influence of MAs. (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16) Many of them, however, implicitly or explicitly assume that exercise intensity is constant or changes slightly. It is, therefore, difficult to simply apply these techniques to heart rate estimation during exercise especially when exercise intensity changes dynamically.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Heart Rate during Exercise from a Photoplethysmographic Signal Considering Exercise Intensity

2016

Sensors and Materials

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Heart rate estimation during exercise with photoplethysmographic (PPG) sensors is a difficult problem because motion artifacts (MAs) contaminate PPG signals. In this paper, we propose a method of accurately estimating heart rate during exercise from a PPG signal by considering exercise intensity. The exercise intensity is estimated from acceleration signals simultaneously recorded with the PPG signal. The proposed method dynamically limits the estimation range of heart rate depending on the exercise intensity, resulting in an accurate heart rate estimation during exercise. From the experimental results, we have confirmed that the proposed method can estimate heart rate more accurately than conventional methods.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Heart Rate during Exercise from a Photoplethysmographic Signal Considering Exercise Intensity

2016

Sensors and Materials

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“…For the IHR, methods include time-frequency (TF) analyses (Gil et al, 2010; Mullan et al, 2015; Wu et al, 2016), adaptive filtering (Yousefi et al, 2014; Khan et al, 2015; Murthy et al, 2015; Schack et al, 2015; Mashhadi et al, 2016), Kalman filter (Frigo et al, 2015), sparse spectrum reconstruction (Zhang, 2015), blind source separation (Wedekind et al, 2015), a Bayesian approach (D'souza et al, 2015; Sun and Zhang, 2015), correntropy spectral density (CSD) (Garde et al, 2014), empirical mode decomposition (EMD) (Zhang et al, 2015), model fitting (Wadehn et al, 2015), deep learning (Jindal, 2016), fusion approaches (Temko, 2015; Zhu S. et al, 2015), etc. For the IRR, efforts include TF analysis (Chon et al, 2009; Orini et al, 2011; Dehkordi et al, 2015), sparse signal reconstruction (Zong and Jafari, 2015; Zhang and Ding, 2016), neural network (Johansson, 2003), modified multi-scale principal component analysis (Madhav et al, 2013), independent component analysis (Zhou et al, 2006), time-varying autoregressive regression (Lee and Chon, 2010b,a), fusion approaches (Karlen et al, 2013; Cernat et al, 2015), pulse-width variability (Lazaro et al, 2013; Cernat et al, 2014), CSD (Pelaez-Coca et al, 2013; Garde et al, 2014), EMD (Garde et al, 2013), a Bayesian approach (Pimentel et al, 2015; Zhu T. et al, 2015), etc.…”

Section: Introductionmentioning

confidence: 99%

How Nonlinear-Type Time-Frequency Analysis Can Help in Sensing Instantaneous Heart Rate and Instantaneous Respiratory Rate from Photoplethysmography in a Reliable Way

Cicone

2017

Front. Physiol.

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Despite the population of the noninvasive, economic, comfortable, and easy-to-install photoplethysmography (PPG), it is still lacking a mathematically rigorous and stable algorithm which is able to simultaneously extract from a single-channel PPG signal the instantaneous heart rate (IHR) and the instantaneous respiratory rate (IRR). In this paper, a novel algorithm called deppG is provided to tackle this challenge. deppG is composed of two theoretically solid nonlinear-type time-frequency analyses techniques, the de-shape short time Fourier transform and the synchrosqueezing transform, which allows us to extract the instantaneous physiological information from the PPG signal in a reliable way. To test its performance, in addition to validating the algorithm by a simulated signal and discussing the meaning of “instantaneous,” the algorithm is applied to two publicly available batch databases, the Capnobase and the ICASSP 2015 signal processing cup. The former contains PPG signals relative to spontaneous or controlled breathing in static patients, and the latter is made up of PPG signals collected from subjects doing intense physical activities. The accuracies of the estimated IHR and IRR are compared with the ones obtained by other methods, and represent the state-of-the-art in this field of research. The results suggest the potential of deppG to extract instantaneous physiological information from a signal acquired from widely available wearable devices, even when a subject carries out intense physical activities.

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“…GA, artificial bee colony algorithm (ABC), and ant colony optimization algorithms (ACO) were also applied to optimize kernel parameters in RVM [46][47][48]. Regarding time series analysis, RVM has been successful in detecting seizure in electroencephalogram (EEG) signals [49] and forecasting stock index [50], exchange rate [51], nonlinear hydrological time series [52], wind speed [47,53], and the price of electricity [54]. These applications show the superiority of RVM in time series forecasting.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels

Zhou

Guo

et al. 2016

Energies

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Abstract:Crude oil, as one of the most important energy sources in the world, plays a crucial role in global economic events. An accurate prediction for crude oil price is an interesting and challenging task for enterprises, governments, investors, and researchers. To cope with this issue, in this paper, we proposed a method integrating ensemble empirical mode decomposition (EEMD), adaptive particle swarm optimization (APSO), and relevance vector machine (RVM)-namely, EEMD-APSO-RVM-to predict crude oil price based on the "decomposition and ensemble" framework. Specifically, the raw time series of crude oil price were firstly decomposed into several intrinsic mode functions (IMFs) and one residue by EEMD. Then, RVM with combined kernels was applied to predict target value for the residue and each IMF individually. To improve the prediction performance of each component, an extended particle swarm optimization (PSO) was utilized to simultaneously optimize the weights and parameters of single kernels for the combined kernel of RVM. Finally, simple addition was used to aggregate all the predicted results of components into an ensemble result as the final result. Extensive experiments were conducted on the crude oil spot price of the West Texas Intermediate (WTI) to illustrate and evaluate the proposed method. The experimental results are superior to those by several state-of-the-art benchmark methods in terms of root mean squared error (RMSE), mean absolute percent error (MAPE), and directional statistic (Dstat), showing that the proposed EEMD-APSO-RVM is promising for forecasting crude oil price.

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Combining ensemble empirical mode decomposition with spectrum subtraction technique for heart rate monitoring using wrist-type photoplethysmography

Cited by 83 publications

References 18 publications

Estimation of Heart Rate during Exercise from a Photoplethysmographic Signal Considering Exercise Intensity

Estimation of Heart Rate during Exercise from a Photoplethysmographic Signal Considering Exercise Intensity

How Nonlinear-Type Time-Frequency Analysis Can Help in Sensing Instantaneous Heart Rate and Instantaneous Respiratory Rate from Photoplethysmography in a Reliable Way

Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels

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