Comparison and Noise Suppression of the Transmitted and Reflected Photoplethysmography Signals

Li, Suyi; Liu, Lijia; Wu, Jianhua; Tang, Bingyi; Li, Dongsheng

doi:10.1155/2018/4523593

Cited by 18 publications

(11 citation statements)

References 25 publications

(23 reference statements)

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“…Novel flexible sensors for transmission and reflection-mode pulse oximetry show a higher SNR due to a reduction in ambient noise. 76 Algorithms such as the independent component analysis (ICA), 77 Kalman filtering, wavelet denoising, 78 and empirical mode decomposition 79,80 were proposed for removing artifacts in PPG signal; however, these techniques were mainly proposed for scenarios with weak noise.…”

Section: Other Cardiac Arrhythmiasmentioning

confidence: 99%

Photoplethysmography based atrial fibrillation detection: a review

et al. 2020

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Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations-a technology known as photoplethysmography (PPG)-from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.npj Digital Medicine (2020) 3:3 ; https://doi.org/10.1038/s41746-019-0207-9 PHOTOPLETHYSMOGRAPHY PPG signal PPG waveform is generated during a cardiac cycle and typically measured at a peripheral site. Therefore, it is essentially a pulse pressure waveform that originates from the heart contraction and propagates through the vascular tree. As blood flow is controlled

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Section: Other Cardiac Arrhythmiasmentioning

confidence: 99%

Photoplethysmography based atrial fibrillation detection: a review

et al. 2020

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“…In order to validate the reliability of the method, a detection model of sleep apnea based on back-propagation (BP) neural network was set up, with the original pulse signals, the denoised signals were obtained by a median filtering method [12], and the preprocessed signals by using the proposed method were used as the model input, respectively, and the sleep apnea syndrome annotations as model output, which were marked as the models ORIP-Apnea, Denoising-Apnea, and PREP-Apnea, respectively. Before preprocessing, there were 9,720 segments of pulse signal, and 9,606 segments were left after removing noises with the proposed method.…”

Section: Resultsmentioning

confidence: 99%

“…By reconstructing and analyzing the sequence correlation of PPG signals, the adaptive removal of noise was achieved, which provided high-quality pulse signal for the subsequent processing. Li et al [12] proposed a simple real-time denoising method based on double median filter to preprocess the PPG signal, which improved the quality of signals by effectively suppressing the noise and preserving the essential morphological features from PPG signals. Wang et al [13] applied empirical mode decomposition (EMD) to the processing of the dynamic pulse data.…”

Section: Introductionmentioning

confidence: 99%

A Pulse Signal Preprocessing Method Based on the Chauvenet Criterion

Liu

et al. 2019

Computational and Mathematical Methods in Medicine

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Pulse signals are widely used to evaluate the status of the human cardiovascular, respiratory, and circulatory systems. In the process of being collected, the signals are usually interfered by some factors, such as the spike noise and the poor-sensor-contact noise, which have severely affected the accuracy of the subsequent detection models. In recent years, some methods have been applied to processing the above noisy signals, such as dynamic time warping, empirical mode decomposition, autocorrelation, and cross-correlation. Effective as they are, those methods are complex and difficult to implement. It is also found that the noisy signals are tightly related to gross errors. The Chauvenet criterion, one of the gross error discrimination criterions, is highly efficient and widely applicable for being without the complex calculations like decomposition and reconstruction. Therefore, in this study, based on the Chauvenet criterion, a new pulse signal preprocessing method is proposed, in which adaptive thresholds are designed, respectively, to discriminate the abnormal signals caused by spike noise and poor-sensor-contact noise. 81 hours of pulse signals (with a sleep apnea annotated every 30 seconds and 9,720 segments in total) from the MIT-BIH Polysomnographic Database are used in the study, including 35 minutes of poor-sensor-contact noises and 25 minutes of spike noises. The proposed method was used to preprocess the pulse signals, in which 9,684 segments out of a total of 9,720 were correctly discriminated, and the accuracy of the method reached 99.63%. To quantitatively evaluate the noise removal effect, a simulation experiment is conducted to compare the Jaccard Similarity Coefficient (JSC) calculated before and after the noise removal, respectively, and the results show that the preprocessed signal obtains higher JSC, closer to the reference signal, which indicates that the proposed method can effectively improve the signal quality. In order to evaluate the method, three back-propagation (BP) sleep apnea detection models with the same network structure and parameters were established, respectively. Through comparing the recognition rate and the prediction rate of the models, higher rates were obtained by using the proposed method. To prove the efficiency, the comparison experiment between the proposed Chauvenet-based method and a Romanovsky-based method was conducted, and the execution time of the proposed method is much shorter than that of the Romanovsky method. The results suggest that the superiority in execution time of the Chauvenet-based method becomes more significant as the date size increases.

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“…In Salehizadeh's research, iterative motion artifact removal (IMARS) has better performance in heart rate and SpO2 detection in noisy environments [18]. Li also proposed double median filter-based slight motion artifact reduction method in transmitted and reflected PPG [19].…”

Section: Introductionmentioning

confidence: 99%

Pre-processing of Photoplethysmographic Waveform for Amplitude Regularization

Kim

Lee

Shin

2019

J. Electr. Eng. Technol.

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This study is aimed to develop a post-processing method to regularize amplitude of PPG waveforms and evaluate the performance of the algorithm. The proposed procedure for regularizing amplitude consists of extracting the signal envelopes based on time-domain filtering, generating a compensation curve, and reconstructing the waveform. The proposed technique was evaluated with respect to dispersion and waveform fluctuation by means of coefficients of variation (CV) and a scaling exponent of detrended fluctuation analysis (DFA). Other criteria included the detection performances of major features such as upper and lower fiducial points and maximum dV/dt. In developing this method, we used a clinical dataset that comprised 74,225 beats. By applying reconstruction algorithm, the %CV and the scaling exponent of DFA were significantly decreased from 115.4 to 71.4 and from 1.029 to 0.836, respectively in peak amplitude. In valley amplitude, they were also decreased from 38.3 to 13.2 and from 0.993 to 0.713, respectively. Moreover, sensitivity of maximum dV/dt point, upper and lower peak detection are increased by 8.50, 2.72 and 5.21% after reconstruction, respectively. Our results clearly showed that the proposed reconstruction technique could reduce amplitude fluctuation and improve detection of fiducial points.

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Comparison and Noise Suppression of the Transmitted and Reflected Photoplethysmography Signals

Cited by 18 publications

References 25 publications

Photoplethysmography based atrial fibrillation detection: a review

Photoplethysmography based atrial fibrillation detection: a review

A Pulse Signal Preprocessing Method Based on the Chauvenet Criterion

Pre-processing of Photoplethysmographic Waveform for Amplitude Regularization

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