Motion Artifacts In PPG Signals Research Articles

Adaptive Algorithm for Motion Artifacts Removal in Wearable Biomedical Sensors During Physical Exercise

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Motion artifacts (MA) are significant sources of noise in the wearable devices that are used to collect biological information from the human body. Photoplethysmography (PPG) is an example of the sensors embedded in many of these devices. PPG signals are highly influenced by MA, especially when the motion involves the organ that the PPG sensor is attached to. The aim of this paper is to build an adaptive algorithm and multiresolution analysis technique to denoise the raw PPG signals, suppress the MAs and accurately estimate the HR as an example of MA suppression in the wearable devices. The challenge here is to find the right selection considering high accuracy and low-computational complexity. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> Discrete wavelet transform recursive inverse (DWT-RI) adaptive filter algorithm in addition to multiresolution analysis is implemented to suppress MAs and estimate the HR using the simultaneously recorded accelerations in the three dimensions as reference signals for the adaptive algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> The proposed algorithm has a low computational cost and results in an absolute average error of 1.17 beats per minute (BPM) when tested on 12 subjects from a well-known database for this purpose. The performance of the proposed algorithm is compared to the other existing methods in literature used for noise removal and motion artifact suppression. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> DWT-RI adaptive algorithm can successfully suppress motion artifacts in PPG signals with a low computational complexity and outperforms several alternatives in terms of the average absolute error.

PPGMotion: Model-based detection of motion artifacts in photoplethysmography signals

Photoplethysmography (PPG) is used widely in health wearables to monitor biomarkers like heart rate. However, motion activities degrade the quality of the measured PPG signal, thereby reducing the accuracy of heart-rate estimation. Existing state-of-the-art methods for motion detection rely on the semi-periodic structure of PPG to detect the aperiodic motion artifacts, thereby failing in scenarios when motion contamination tends to be periodic. We propose a novel technique, PPGMotion, for detecting all types of motion artifacts in PPG signals with high accuracy, without the need for any reference motion signals. Our approach relies on the morphological structure of the artifact-free PPG signal. We compare our method against some classical methods on one synthetic and four real datasets – dataset (1) and (2) are obtained from finger pulse-oximeter under motion activities, dataset (3) and (4) are obtained from a wearable smartwatch. We show that for the synthetic dataset, the performance of PPGMotion is significantly better than existing work as the contaminated PPG tends to become periodic, with an increase in sensitivity of at least 20% over state-of-the-art methods. For real data, PPGMotion achieves similar performance for random motion artifact detection as the classical methods but performs significantly better when motion tends to be periodic, with at least 10% increase in sensitivity in detecting motion artifacts in datasets (2), (3), and (4).

Detection and Removal of Motion Artifacts in PPG Signals

With the rise of wearable devices, which integrate myriad of health-care and fitness procedures into daily life, a reliable method for measuring various bio-signals in a daily setup is more desired than ever. Many of these physiological parameters, such as Heart rate (HR) and Respiratory Rate (RR), are extracted indirectly and using other signals such as Photoplethysmograph (PPG). Part of the reason is that in some cases, such as RR measurements, the devices which directly measure them are cumbersome to wear and thus, rather impractical. On the other hand, signals, such as PPG from which the RR can be extracted, are not very clean. This poses a challenge on reliable extraction of these metrics. The most important problem is that they are corrupted by motion artifacts. In this paper, we review the state of the art algorithms which are used to detect and filter motion artifacts in PPG signals and compare them in terms of their performance. The insight provided by this paper can help the scientists and engineers to obtain a better understanding of the field and be able to use the most suitable technique for their work, or come up with innovative solutions based on existing ones.

Motion Artifact Identification and Removal From Wearable Reflectance Photoplethysmography Using Piezoelectric Transducer

Accurate heart rate estimation from reflectance photoplethysmography (PPG) during intense physical activity is difficult given unpredictable motion artifacts (MAs). Recent research has focused on the removal of MAs in PPG signals by considering acceleration signals as noise references. However, this approach does not always effectively remove MAs, as they can be associated with other noise sources. For instance, finger or hand motion reflects upon the wrist, corrupting the PPG signals, but an accelerometer cannot capture this type of MA. Hence, we propose adding a piezoelectric transducer on the wrist to identify noise originated from finger and hand motion. We first detail the developed wearable device that simultaneously measures PPG, acceleration, and piezoelectric pressure signals from the wrist. Then, the effectiveness of piezoelectric pressure signals for MA removal is verified from PPG signals collected under finger tapping and hand folding/unfolding, and from data of 17 healthy subjects undergoing physical activity. MA removal using both acceleration and piezoelectric pressure signals exhibits a mean absolute error of 1.58 beats per minute (bpm), considerably below the error of 3.75 bpm obtained using only acceleration signals. Therefore, measuring piezoelectric pressure signals on the wrist can improve heart rate estimation by providing more information of MAs corrupting PPG signals.

PPG signal motion artifacts correction algorithm based on feature estimation

In order to reduce the interference of human motion artifacts in PPG signals, when a reflective detector is used to extract signals, a novel motion artifacts correction algorithm based on Gaussian function decomposition and minimum mean square error estimation is proposed. Firstly, band pass filter and notch filter are used to reduce signal burr and interference of power frequency signal to get more accurate and smooth PPG signal, at same time, Kurtosis, Skewness, Standard Deviation are used to evaluate whether motion artifacts exist in a long series of PPG signals. Secondly, an improved sliding window method is used to detect the wave peaks and troughs of PPG signal and, then, take them as features of the PPG signal. Thirdly, according to extracted temporal features of quality PPG signals, the characteristics of the disturbed PPG signals by motion artifacts are estimated employing minimum mean square error estimation and corrected high-quality PPG signals are synthesized using the Gauss function. The experiment are carried out with human physiological signal extraction system designed by ourselves to show that the algorithm proposed in this paper can eliminate motion artifacts in PPG signal sequence well and human physiological parameters are calculated more accurate.

Study on the utilization of acceleration for reduction of body motion artifacts in PPG signals

Study on the utilization of acceleration for reduction of body motion artifacts in PPG signals

Heart Rate Monitoring Using a Slow–Fast Adaptive Comb Filter to Eliminate Motion Artifacts

This paper proposes an algorithm for eliminating motion artifacts in heart rate monitoring using a photoplethysmography (PPG) sensor. No additional hardware, such as accelerometers or other reference signals, is needed. The algorithm improves the quality of PPG signals by implementing a comb filter during signal processing. The proposed slow–fast adaptive comb filter (SFACF) distinguishes motion artifacts in PPG signals based on harmonic and continuous features of the heart rate. The effectiveness of fundamental and harmonic frequency enhancement in real time is experimentally demonstrated, with the heart rate updated every second. The performance of the SFACF, which does not require additional reference signals, is compared with those of adaptive noise cancellation (ANC) and spectral peak search-comb filtering (SPS-CF). It is found that for a low-quality PPG signal, the SFACF outperforms ANC and SPS-CF. Finally, measurements obtained using a commercial heart rate monitor and the proposed algorithm are shown to be highly correlated.

A wrist worn SpO2 monitor with custom finger probe for motion artifact removal.

Continuous monitoring of blood oxygen saturation (SpO2) level and heart rate is critical in surgery, ICUs and patients suffering from Chronic Obstructive Pulmonary Diseases. Pulse oximeters which compute SpO2 using transmittance photoplethysmography (PPG), is widely accepted for continuous monitoring. Presence of motion artifacts in PPG signals is a major obstacle in the extraction of reliable cardiovascular parameters, in real time and continuous monitoring applications. In this paper, a wrist worn device with a custom finger probe with an integrated accelerometer to remove motion artifacts is presented. An algorithm which can run on low power systems with processing constraints is implemented on the device. The device does continuous acquisition of PPG and accelerometer waveforms and computes SpO2 using the proposed light weight algorithm. The measurement results are continuously synced with an Android tablet, which acts as a gateway and is pushed on to the cloud for further analysis. The accuracy in SpO2 measured by the device was validated using Fluke ProSim 8 SpO2 simulator and the efficiency in accurately computing SpO2 in the presence of motion was validated over 40 healthy volunteers in a controlled setting.

A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor.

Accurate estimation of heart rates from photoplethysmogram (PPG) signals during intense physical activity is a very challenging problem. This is because strenuous and high intensity exercise can result in severe motion artifacts in PPG signals, making accurate heart rate (HR) estimation difficult. In this study we investigated a novel technique to accurately reconstruct motion-corrupted PPG signals and HR based on time-varying spectral analysis. The algorithm is called Spectral filter algorithm for Motion Artifacts and heart rate reconstruction (SpaMA). The idea is to calculate the power spectral density of both PPG and accelerometer signals for each time shift of a windowed data segment. By comparing time-varying spectra of PPG and accelerometer data, those frequency peaks resulting from motion artifacts can be distinguished from the PPG spectrum. The SpaMA approach was applied to three different datasets and four types of activities: (1) training datasets from the 2015 IEEE Signal Process. Cup Database recorded from 12 subjects while performing treadmill exercise from 1 km/h to 15 km/h; (2) test datasets from the 2015 IEEE Signal Process. Cup Database recorded from 11 subjects while performing forearm and upper arm exercise. (3) Chon Lab dataset including 10 min recordings from 10 subjects during treadmill exercise. The ECG signals from all three datasets provided the reference HRs which were used to determine the accuracy of our SpaMA algorithm. The performance of the SpaMA approach was calculated by computing the mean absolute error between the estimated HR from the PPG and the reference HR from the ECG. The average estimation errors using our method on the first, second and third datasets are 0.89, 1.93 and 1.38 beats/min respectively, while the overall error on all 33 subjects is 1.86 beats/min and the performance on only treadmill experiment datasets (22 subjects) is 1.11 beats/min. Moreover, it was found that dynamics of heart rate variability can be accurately captured using the algorithm where the mean Pearson’s correlation coefficient between the power spectral densities of the reference and the reconstructed heart rate time series was found to be 0.98. These results show that the SpaMA method has a potential for PPG-based HR monitoring in wearable devices for fitness tracking and health monitoring during intense physical activities.

A Novel Approach for Motion Artifact Reduction in PPG Signals Based on AS-LMS Adaptive Filter

The performance of pulse oximeters is highly influenced by motion artifacts (MAs) in photoplethysmographic (PPG) signals. In this paper, we propose a simple and efficient approach based on adaptive step-size least mean squares (AS-LMS) adaptive filter for reducing MA in corrupted PPG signals. The presented method is an extension to our prior work on efficient use of adaptive filters for reduction of MA in PPG signals. The novelty of the method lies in the fact that a synthetic noise reference signal for an adaptive filtering process, representing MA noise, is generated internally from the MA-corrupted PPG signal itself instead of using any additional hardware such as accelerometer or source-detector pair for acquiring noise reference signal. Thus, the generated noise reference signal is then applied to the AS-LMS adaptive filter for artifact removal. While experimental results proved the efficacy of the proposed scheme, the merit of the method is clearly demonstrated using convergence and correlation analysis, thus making it best suitable for present-day pulse oximeters utilizing PPG sensor head with a single pair of source and detector, which does not have any extra hardware meant for capturing noise reference signal. In addition to arterial oxygen saturation estimation, the artifact reduction method facilitated the waveform contour analysis on artifact-reduced PPG, and the conventional parameters were evaluated for assessing the arterial stiffness.