Research Article  Open Access
Comparison and Noise Suppression of the Transmitted and Reflected Photoplethysmography Signals
Abstract
The photoplethysmography (PPG) is inevitably corrupted by many kinds of noise no matter whether its acquisition mode is transmittance or reflectance. To enhance the quality of PPG signals, many studies have made great progress in PPG denoising by adding extra sensors and developing complex algorithms. Considering the reasonable cost, compact size, and realtime and easy implementation, this study proposed a simple realtime denoising method based on double median filters which can be integrated in microcontroller of commercial or portable pulse oximeters without adding extra hardware. First, we used the boundary extension to preserve the signal boundary distortion and designed a first median filter with the time window at approximately 78 ms to eliminate the highfrequency components of the signal. Then, through the second median filter with a time window which was about 780 ms, we estimated the lowfrequency components. Finally, we removed the estimated lowfrequency components from the signal to obtain the denoised signal. Through comparing the multiple sets of signals under calmly sitting and slightly moving postures, the PPG signals contained noises no matter whether collected by the transmittancemode or the reflectancemode. To evaluate the proposed method, we conducted measured, simulated experiments and a strong noisy environment experiment. Through comparing the morphology distortions, frequency spectra, and the signaltonoise ratios (SNRs), the results showed that the proposed method can suppress noise effectively and preserve the essential morphological features from PPG signals. As a result, the proposed method can enhance the quality of PPG signals and, thus, can contribute to the improvement of the calculation accuracy of the subsequent physiological parameters. In addition, the proposed method could be a good choice to address the realtime noise reduction of portable PPG measuring instruments.
1. Introduction
The initial clinical application of photoplethysmography (PPG) is to monitor the noninvasive blood oxygen saturation (SpO_{2}) and, furthermore, it has been widely used in the assessment of the cardiovascular, respiratory, and hematological status [1, 2].
The principle of PPG acquisition is to emit different wavelengths of LED lights on the epidermis and to receive the transmitted or reflected lights by using the photoelectric sensors. The received optical density will vary as the blood volume of the measuring site changes and can be recorded by using electrical signals to form PPG which can be used to derive the approximate formula for SpO_{2} estimation based on Lambert–Beer law [3, 4]. Compared with the fingertip transmittancemode PPG monitoring, the measuring site and the motion status are relatively flexible under the reflectancemode, and studies have shown that the reflectancemode way can achieve better SpO_{2} measurement accuracy during the perfusion [5, 6]. However, fingertip transmittance pulse oximeters are still playing the important role in clinical applications because of their stable performance, easy operation, and low cost [7].
No matter whether the PPG signal is obtained by using transmittancemode or reflectancemode, it will inevitably be corrupted by many kinds of noise such as highfrequency noise, power line interference, baseline drift, and motion artifact (MA), and these noises will affect the pulse rate analysis and SpO_{2} measurement accuracy [8]. To date, many studies have proposed noise suppression methods for PPG signals. The highfrequency noise in PPG signal could be eliminated by empirical mode decomposition (EMD) method [9]. The power line interference could be reduced by a wavelet denoising method integrated in DSP [10]. The baseline drift could be estimated by applying the wavelet multiresolution principle [11, 12]. A realtime method based on a contour analysis was implemented on a 32bit ARM core microcontroller to detect the pulse waveform segmentation and artifact [13]. Due to the fact that MA can significantly distort the morphology of the PPG signal, it is worthwhile to focus on the removal methods. One of the commonly used approaches is through adding the extra hardware, such as using the accelerometer as the reference signal for MA cancellation [14–16]. Another way is through designing the denoising algorithms, including adaptive filter [17, 18], waveletbased method [19, 20], independent component analysis (ICA) [21, 22], singular value decomposition (SVD) [23], cyclebycycle Fourier series analysis [24, 25], and higher order statistics [26].
We have seen the significant progress in the domain of PPG denoising. However, the above methods include adding extra hardware and requiring highend microcontroller or host computer where the complicated algorithm needs to run and the PPG data needs to be transferred to, which are all complicated and can be inapplicable due to the high market share of the traditional pulse oximeter. Therefore, considering the low cost, compact size, and realtime and easy implementation, a denoising method based on double median filters is proposed in this study. The proposed denoising method can run on the ordinary microcontroller and can be realtime. In other words, it is convenient enough to be embedded in commercial twowavelength pulse oximeters without changing any hardware or transmitting the data to the host computer for processing.
This study will first compare the reflected PPG with the transmitted PPG signals under calmly sitting and slightly moving postures and describe the principle of the denoising method based on double median filters and its implementation steps. Then, we will conduct experiments using measured signals, simulated signals, and the noisy signals collected under the strong noise environment, respectively. Finally, we will evaluate the performance of the proposed denoising method through comparing the morphology distortions, frequency spectra, and SNRs.
2. Materials and Methods
2.1. The Measurement Devices
Most pulse oximeters are designed based on the good linear relationship between the oxygen saturation and the relative light intensity of the 660 nm (redlight) and 940 nm (IRlight) wavelengths received by the photodetector. The reflective pulse oximeter used in the experiment was provided by Tianjin Synopsis Technology Co., Ltd., China. The raw data can be collected by the data acquisition software provided by the company with the sampling frequency of 100 Hz. The emitter and photodetector are adjacent to each other with the measuring site side by side, as shown in Figure 1(a). The prototype of transmitted pulse oximeter was developed by Jilin University, China, with the sampling frequency of 128 Hz. The emitter and photodetector are opposite to each other with the measuring site inbetween, as shown in Figure 1(b). When the redlight and IRlight pass through the measuring site, they will be received by the photodetector to produce PPG signals, as shown in Figure 1.
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2.2. PPG Signals Acquisition
A total of 10 volunteers participated in the experiment, 7 males and 3 females. The mean age (mean ± std) was 26.20 ± 5.14 and the mean body mass index (BMI ± std) was 21.79 ± 3.40. The volunteers were informed about the study before the data was obtained. Table 1 shows their basic personal information.

We used the above two devices to collect their multiple sets of middle fingertips PPG signals under the calmly sitting and slightly moving postures. Hardware filters can eliminate some of the noise in PPG signals, but the signal was still affected by respiration, random noise, and motion artifacts during the measuring procedure, resulting in morphological distortions in PPG signals. For the clarity of the subsequent comparisons, the amplitude range of the signal was normalized from 0 to 1 by usingwhere , is the data length, is the normalized signal, and is the raw signal.
We employed 2000 representative samples which were taken from the IR signals acquired by the transmittance and by the reflectance oximeter, respectively, as shown in Figure 2. The quality of the PPG signal under the sitting posture is relatively good. Figure 2(a) illustrates the transmitted signal which contained a small amount of highfrequency noise; Figure 2(b) shows the reflected PPG signal with mild baseline wander which may be caused by respiration. Under the slightly moving posture, the morphologies of the transmitted and reflected signal are both distorted due to the motion artifacts, as shown in Figures 2(c) and 2(d), respectively. Therefore, it is necessary to suppress the noise and to preserve the essential morphological features, enhancing the signal quality to improve the calculation accuracy of the physiological parameters subsequently.
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2.3. The Denoising Method Based on Double Median Filters
According to the frequency of the major component of the PPG signal and the spectral comparisons of the reflected and the transmitted signals under the calmly sitting and slightly moving postures, we designed a noise reduction method based on double median filters.
The median filter is a nonlinear digital filter technique which is very widely used to eliminate noise from digital signals. It works by moving through the signal entry by entry, replacing each entry with the median of neighboring entries. The pattern of neighbors is called the “window,” which slides, entry by entry, over the entire signal [27]. The window size is the key step of the median filter design. In general, the larger the window is, the lower the frequency of the fitted signal is.
Through the simulation and measurement experiments, the highfrequency noise can be suppressed effectively when the window size is from 9 to 12 samples under the sampling frequency of 128 Hz; the lowfrequency noise introduced mainly by respiration and movement can be reduced better when the window size is from 96 to 110.
In this application, the window size of the first median filter, , was set as 10 samples, corresponding approximately to 78 ms; that of the second median filter, , was set as 100 samples, corresponding approximately to 780 ms. The diagram of the denoising method is shown in Figure 3.
The main steps of the method are shown as follows.
The raw signal after normalization is denoted as . In order to prevent the boundary distortion, we used the boundary extension to process . The extension length is related to the size of the window; here we chose , and the signal after extension is recorded as , shown in
The first median filter with the window size was used to eliminate the highfrequency noise from ; then the signal after the processing is recorded as , and the value of equals the one from the sequence whose value corresponds to the minimum of the expression , where , .
The second median filter with the window size could be used to estimate the lowfrequency noise of which is recorded as ; then the value of equals the one from the sequence whose value corresponds to the minimum of the expression , where , .
The denoised signal can be obtained by subtracting from , as shown in
3. Results and Discussion
3.1. The Experiment for Measured Signals
In the measurement experiment, the above two devices were used to collect multiple sets of transmitted and reflected PPG signals, of which the 10 subjects were under the calmly sitting and slightly moving postures. For convenient comparison, we continued to use the signals in Figure 2 to represent the denoising results.
The red curves in Figure 4 are the noise suppressed by using our proposed method. Figure 4(a) is the raw transmitted PPG signal (blue curve) under the sitting posture, named as TPPGS, and Figure 4(b) is that under the slightly moving posture, named as TPPGM; their corresponding denoised signals are shown, respectively, in Figure 4(c), named as DTPPGS, and in Figure 4(d), named as DTPPGM.
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Figure 4(e) is the raw reflected PPG signal (blue curve) under the sitting posture, named as RPPGS, and Figure 4(f) is that under the slightly moving posture, named as RPPGM; their corresponding denoised signals are shown in Figure 4(g), named as DRPPGS, and in Figure 4(h), named as DRPPGM.
From visual comparison of the morphology and the smoothness of the signals before and after denoising, the noise is well suppressed, and the quality of the PPG signals is improved by the denoising method.
For quantitative evaluation of the method, we performed spectral analysis of the PPG signals from Figure 4. The frequency of the major component of PPG signal is generally concentrated in 0.510 Hz; hence, the frequency values plotted in Figure 5 are set from 0 to 30 Hz for convenient observation. Figures 5(a)–5(h) correspond to the spectra of TPPGS, of DTPPGS, of TPPGM, of DTPPGM, of RPPGS, of DRPPGS, of RPPGM, and of DRPPGM, respectively. It can be found that the high frequency near 25 Hz and the low frequency near 0.5 Hz are well eliminated.
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3.2. The Experiment for Simulated Signals
We compared the denoising effects of the proposed method and a waveletbased method. The waveletbased method can remove baseline wander as well as partial motion artifacts effectively. However, it should be run on the host computer and it is not realtime. We used a fraction of good quality signals to be the reference signal. The noisy signals could be synthesized by adding simulated noise to the reference signal. Then, the two denoising methods were compared by using SNR.
We used 2000 representative samples from good quality reflected signals to be the reference signal, as shown in Figure 6(a). The simulated noise for baseline wander and partial motion artifacts is shown in Figure 6(b). The simulated noise being superposed on the reference signal synthesized a noisy signal, which is shown in Figure 6(c). We continue to add 10 dB white Gaussian noise to synthesize another noisy signal which contains not only the baseline wander and partial motion artifacts, but also the random noise, as shown in Figure 6(d). After using the waveletbased method (please see [11, 12] for details), the denoised signal of Figure 6(c) is shown in Figure 6(e), and that of Figure 6(d) is shown in Figure 6(f). After using our proposed method, the denoised signal of Figure 6(c) is shown in Figure 6(g), and that of Figure 6(d) is shown in Figure 6(h).
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The corresponding SNRs of the signals from Figure 6 were calculated. The comparison results are listed in Table 2.

The SNR calculated by using the noisy signal in Figure 6(c) is 7.6857 and those using the denoised signals in Figures 6(e) and 6(g) are 16.2620 and 12.7598, respectively; the SNR calculated by using the noisy signal in Figure 6(d) is 5.7739 and those using the denoised signals in Figures 6(f) and 6(h) are 6.1874 and 11.3860, respectively.
By comparing the morphology in Figure 6 and the SNR in Table 2, we can see that the quality of the signal is improved after denoising. Comparing Figures 6(c), 6(e), and 6(g), the noise is reduced well. Although the denoised effect by the waveletbased method (SNR is 16.2620) is better than that by the proposed method (SNR is 12.7598), the waveletbased method needs to run on the host computer and is not realtime. Comparing Figures 6(d), 6(f), and 6(h), the waveletbased method is good at reducing that kind of lowfrequency noise, but not the random noise, and the denoised effect of the proposed method (SNR is 11.3860) is much better than that by the waveletbased method (SNR is 6.1874).
3.3. The Experiment for Strong Noisy Signals
During the experiment, it happened to start a suction electromagnetic vibrator that greatly increased the environmental noise in the measurement. At that time, we collected the raw signal by using the reflective oximeter. The representative 2000 samples are shown in Figure 7(a). Figure 7(b) is the spectrum of the raw signal, and the noise is severe in the signal. Figure 7(c) is the denoised signal using the method based on double median filters, and Figure 7(d) is the spectrum of the denoised signal. By comparing the morphology and spectrum of the signal before and after denoising, the validity of the denoising method is further verified.
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The above denoising experiments used measured signals, simulated signals, and the strong noisy signals. By comparing the morphology, frequency spectrum, and SNR of the signals before and after denoising, the results demonstrated the effectiveness and practicability of the proposed method.
4. Conclusion
In this study, a simple, realtime PPG denoising method based on double median filters was proposed, which could be integrated in microcontroller of commercial or portable pulse oximeters without adding extra hardware. The experiments were conducted using measured signals, simulated signals, and the noisy signals collected under the strong noise environment. Through evaluating the denoising effects (morphology, frequency spectrum, and SNR comparisons of the signals), the experimental results showed that the proposed method can remove the noise well and enhance the quality of PPG signals. The proposed method has the potential to improve the calculation accuracy of the subsequent physiological parameters and can be a convenient solution to the realtime noise suppression for portable pulse oximeters as well. For future research, we will collect more PPG signals from volunteers to compare the noise reduction effects through the calculation accuracies of physiological parameters (such as HR and SpO_{2}) before and after denoising using the proposed method and traditional methods. And then, results will further reveal the feasibility and possibility of embedding the proposed method into commercial portable pulse oximeters.
Abbreviations
PPG:  Photoplethysmography 
SpO_{2}:  Noninvasive blood oxygen saturation 
MA:  Motion artifact 
EMD:  Empirical mode decomposition 
ICA:  Independent component analysis 
SVD:  Singular value decomposition 
BMI:  Body mass index 
SNR:  Signaltonoise ratio 
TPPGS:  Transmitted PPG signal 
DTPPGS:  Denoised transmitted PPG signal 
TPPGM:  Transmitted PPG signal under moving 
DTPPGM:  Denoised transmitted PPG signal under moving 
RPPGS:  Reflected PPG signal 
DRPPGS:  Denoised reflected PPG signal 
RPPGM:  Reflected PPG signal under moving 
DRPPGM:  Denoised reflected PPG signal under moving. 
Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.
Ethical Approval
We had received ethical approval from Jilin University to conduct the experiments.
Consent
The measurements were noninvasive and without any risk on volunteers. All volunteers had signed an informed consent for participation in the measurements.
Conflicts of Interest
The authors declare that they have no competing interests.
Authors’ Contributions
Suyi Li conceived this study and drafted the paper; Suyi Li and Jiang Wu designed the realtime denoising method based on double median filters; Dongsheng Li, Lijia Liu, and Bingyi Tang carried out the experiment and proved the validity of the method; all authors contributed to the discussion of the work and approved the final paper.
Acknowledgments
This study was supported by the National Key Technologies R&D Program of China (No. 2017YFC0307700) and National Natural Science Foundation of Jilin Province, China (No. 20180101049JC). The authors thank the members of the projects committee for their help.
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Copyright
Copyright © 2018 Suyi Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.