Combining Nonlinear Adaptive Filtering and Signal Decomposition for Motion Artifact Removal in Wearable Photoplethysmography

Ye, Yalan; Cheng, Yunfei; He, Wei; Hou, Mengshu; Zhang, Zhilin

doi:10.1109/jsen.2016.2597265

Cited by 88 publications

(55 citation statements)

References 21 publications

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“…Movement artifacts affect both the PPG and noninvasive BP. Although noise reduction in the PPG is an active field of study, sophisticated techniques of signal reconstruction during movement have focused on accurate estimations of heart rate [39], [40]. PPG motion artifact reduction remains a challenging task.…”

Section: Limitations and Future Studiesmentioning

confidence: 99%

Non-Invasive Detection of Mechanical Alternans Utilizing Photoplethysmography

Besleaga

Badiani

Lloyd

et al. 2019

IEEE J. Biomed. Health Inform.

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Background and Significance: Mechanical alternans (MA) is a biomarker associated with mortality and life-threatening arrhythmias in heart failure patients. Despite showing prognostic value, its use is limited by the requirement of measuring continuous blood pressure (BP), which is costly and impractical. Objective: To develop and test, for the first time, non-invasive MA surrogates based on photoplethysmography (PPG). Methods: Continuous BP and PPG were recorded during clinical procedures and tests in 35 patients. MA was induced either by ventricular pacing (Group A, N=19) or exercise (Group B, N=16). MA was categorized as sustained or intermittent if MA episodes were observed in at least 20 or between 12 to 20 consecutive beats, respectively. Eight features characterizing pulse morphology were derived from the PPG and MA surrogates were evaluated. Results: Sustained alternans was observed in 9 patients (47%) from Group A, whereas intermittent alternans was observed in 13 patients (68%) from Group A and in 10 patients (63%) from Group B. The PPG-based MA surrogate showing the highest accuracy, V'M, was based on the maximum of the first derivative of the PPG pulse. It detected both sustained and intermittent MA with 100% sensitivity and 100% specificity in Group A and intermittent MA with 100% sensitivity and 83% specificity in Group B. Furthermore, the magnitudes of MA and its PPG-based surrogate were linearly correlated (R 2 =0.83, p<0.001). Conclusion: MA can be accurately identified non-invasively through PPG analysis. This may have important clinical implications for risk stratification and remote monitoring.

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Section: Limitations and Future Studiesmentioning

confidence: 99%

Non-Invasive Detection of Mechanical Alternans Utilizing Photoplethysmography

Besleaga

Badiani

Lloyd

et al. 2019

IEEE J. Biomed. Health Inform.

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“…The sampling rate of signals is 125 Hz. Further information on the data conditions are presented in [ 22 , 24 , 30 , 31 ].…”

Section: Datasets and Performance Measurementioning

confidence: 99%

“…Recently, the approach, which is based on signal decomposition [ 22 , 23 , 24 ], demonstrates the best performance among the existing approaches but because its computational load is too heavy, it cannot be applied to the wearable devices requiring real-time processing. Furthermore, the approach presented in [ 24 ] employs the second-order Volterra filter to nonlinearly model motion artifacts with signal decomposition. This shows the possibility on the nonlinear relationship between the motion artifact interference and the acceleration data.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Quadratic Nonlinearity between Motion Artifact and Acceleration Data and its Application to Heartbeat Rate Estimation

Kim

Park

2017

Sensors

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Accelerometers are applied to various applications to collect information about movements of other sensors deployed at diverse fields ranging from underwater area to human body. In this study, we try to characterize the nonlinear relationship between motion artifact and acceleration data. The cross bicoherence test and the Volterra filter are used as the approaches to detection and modeling. We use the cross bicoherence test to directly detect in the frequency domain and we indirectly identify the nonlinear relationship by improving the performance of eliminating motion artifact in heartbeat rate estimation using a nonlinear filter, the second-order Volterra filter. In the experiments, significant bicoherence values are observed through the cross bicoherence test between the photoplethysmogram (PPG) signal contaminated with motion artifact and the acceleration sensor data. It is observed that for each dataset, the heartbeat rate estimation based on the Volterra filter is superior to that of the linear filter in terms of average absolute error. Furthermore, the leave one out cross-validation (LOOCV) is employed to develop an optimal structure of the Volterra filter for the total datasets. Due to lack of data, the developed Volterra filter does not demonstrate significant difference from the optimal linear filter in terms of t-test. Through this study, it can be concluded that motion artifact may have a quadaratical relationship with acceleration data in terms of bicoherence and more experimental data are required for developing a robust and efficient model for the relationship.

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“…1). At present, many techniques were proposed to reduce MA from PPG signal during movement, including Wavelet Denoising [8], [9], Independ Component Analysis (ICA) [10], [11], Singular Spectrum Analysis (SSA) [12], [13], Empirical Mode Decomposition (EMD) [14], [15], and [16], Kalman Filtering [17], Adaptive Filtering [18], [19], Particle Filtering [20], [21] and other mixture of methods [22], [23], etc.…”

Section: Introductionmentioning

confidence: 99%

Combining Parallel Adaptive Filtering and Wavelet Threshold Denoising for Photoplethysmography-Based Pulse Rate Monitoring during Intensive Physical Exercise

Wan

Chen

Yang

et al. 2020

IEICE Trans. Inf. & Syst.

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Real-time pulse rate (PR) monitoring based on photoplethysmography (PPG) has been drawn much attention in recent years. However, PPG signal detected under movement is easily affected by random noises, especially motion artifacts (MA), affecting the accuracy of PR estimation. In this paper, a parallel method structure is proposed, which effectively combines wavelet threshold denoising with recursive least squares (RLS) adaptive filtering to remove interference signals, and uses spectral peak tracking algorithm to estimate real-time PR. Furthermore, we propose a parallel structure RLS adaptive filtering to increase the amplitude of spectral peak associated with PR for PR estimation. This method is evaluated by using the PPG datasets of the 2015 IEEE Signal Processing Cup. Experimental results on the 12 training datasets during subjects' walking or running show that the average absolute error (AAE) is 1.08 beats per minute (BPM) and standard deviation (SD) is 1.45 BPM. In addition, the AAE of PR on the 10 testing datasets during subjects' fast running accompanied with wrist movements can reach 2.90 BPM. Furthermore, the results indicate that the proposed approach keeps high estimation accuracy of PPG signal even with strong MA.

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Combining Nonlinear Adaptive Filtering and Signal Decomposition for Motion Artifact Removal in Wearable Photoplethysmography

Cited by 88 publications

References 21 publications

Non-Invasive Detection of Mechanical Alternans Utilizing Photoplethysmography

Non-Invasive Detection of Mechanical Alternans Utilizing Photoplethysmography

Characterization of Quadratic Nonlinearity between Motion Artifact and Acceleration Data and its Application to Heartbeat Rate Estimation

Combining Parallel Adaptive Filtering and Wavelet Threshold Denoising for Photoplethysmography-Based Pulse Rate Monitoring during Intensive Physical Exercise

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