A Novel Personalized Motion and Noise Artifact (MNA) Detection Method for Smartphone Photoplethysmograph (PPG) Signals

Tabei, Fatemehsadat; Kumar, Rajnish; Phan, Tra Nguyen; McManus, David D.; Chong, Jo Woon

doi:10.1109/access.2018.2875873

Cited by 30 publications

(26 citation statements)

References 72 publications

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“…The following SQIs are considered to quantify signal quality in this paper. 1) standard deviation of instantaneous heart rate ( STD – HR ), 2) root mean square of the successive differences of peak-to-peak time intervals ( RMSSD – T ), and 3) standard deviation of peak values ( STD – PV ) [11, 33, 45, 46].…”

Section: Methodsmentioning

confidence: 99%

A novel diversity method for smartphone camera-based heart rhythm signals in the presence of motion and noise artifacts

et al. 2019

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The advent of smartphones has advanced the use of embedded sensors to acquire various physiological information. For example, smartphone camera sensors and accelerometers can provide heart rhythm signals to the subjects, while microphones can give respiratory signals. However, the acquired smartphone-based physiological signals are more vulnerable to motion and noise artifacts (MNAs) compared to using medical devices, since subjects need to hold the smartphone with proper contact to the smartphone camera and lens stably and tightly for a duration of time without any movement in the hand or finger. This results in more MNA than traditional methods, such as placing a finger inside a tightly enclosed pulse oximeter to get PPG signals, which provides stable contact between the sensor and the subject’s finger. Moreover, a smartphone lens does not block ambient light in an effective way, while pulse oximeters are designed to block the ambient light effectively. In this paper, we propose a novel diversity method for smartphone signals that reduces the effect of MNAs during heart rhythm signal detection by 1) acquiring two heterogeneous signals from a color intensity signal and a fingertip movement signal, and 2) selecting the less MNA-corrupted signal of the two signals. The proposed method has advantages in that 1) diversity gain can be obtained from the two heterogeneous signals when one signal is clean while the other signal is corrupted, and 2) acquisition of the two heterogeneous signals does not double the acquisition procedure but maintains a single acquisition procedure, since two heterogeneous signals can be obtained from a single smartphone camera recording. In our diversity method, we propose to choose the better signal based on the signal quality indices (SQIs), i.e., standard deviation of instantaneous heart rate ( STD – HR ), root mean square of the successive differences of peak-to-peak time intervals ( RMSSD – T ), and standard deviation of peak values ( STD – PV ). As a performance metric evaluating the proposed diversity method, the ratio of usable period is considered. Experimental results show that our diversity method increases the usable period 19.53% and 6.25% compared to the color intensity or the fingertip movement signals only, respectively.

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Section: Methodsmentioning

confidence: 99%

A novel diversity method for smartphone camera-based heart rhythm signals in the presence of motion and noise artifacts

et al. 2019

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“…When the light falls on the skin, it will be detected by the photodiode in two modes: transmission mode where the light source and the photo-detector are on the opposite side of each other, and reflection mode where the light source and the photo-detector are on the same side. In our scenario, light from the smartphone's flash is captured by the camera on the same side due to reflections from the tissue, bones and blood vessels of the finger [17], [18]. Therefore, the original PPG signals retrieved from the recorded video are reversed and need to be inverted to recover to normal case as shown in Fig.…”

Section: Raw Ppg Signal Preprocessingmentioning

confidence: 99%

A Noninvasive Blood Glucose Monitoring System Based on Smartphone PPG Signal Processing and Machine Learning

Zhang

Mei

Zhang

et al. 2020

IEEE Trans. Ind. Inf.

118

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Blood glucose needs to be monitored on a regular basis to prevent diabetes from consuming the health of hyperglycemic patients. Currently in clinic, it is measured using an invasive technique which is uncomfortable and has risky of infection. To facilitate daily care at home, we propose an intelligent, non-invasive blood glucose monitoring system which can differentiate a user's blood glucose level into normal, borderline and warning based on smartphone PPG signals. The main implementation processes of the proposed system include: (1) a novel algorithm for acquiring photoplethysmography (PPG) signals using only smartphone camera videos; (2) a fitting-based sliding window (FSW) algorithm to remove varying degrees of baseline drifts and segment the signal into single periods; (3) extracting characteristic features from the Gaussian functions by comparing PPG signals at different blood glucose levels; (4) categorizing the valid samples into three glucose levels by applying machine learning algorithms. Our proposed system was evaluated on a data set of 80 subjects. Experimental results demonstrate that the system can separate valid signals from invalid ones at an accuracy of 97.54% and the overall accuracy of estimating the blood glucose levels reaches 81.49%. The proposed system provides a reference for the introduction of non-invasive blood glucose technology into daily or clinical applications. This research also indicates that smartphone-based PPG signals have great potentiality to assess individual's blood glucose level.

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“…Several studies have been conducted to detect artifacts in PPG signals obtained from smartphones [8,9]. In [8], invalid PPG signals were detected due to hand movement.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Fingertip Photoplethysmographic Signal Artifact Detection Method: A Machine Learning Approach

Athaya

Choi

2021

Journal of Sensors

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A photoplethysmography method has recently been widely used to noninvasively measure blood volume changes during a cardiac cycle. Photoplethysmogram (PPG) signals are sensitive to artifacts that negatively impact the accuracy of many important measurements. In this paper, we propose an efficient system for detecting PPG signal artifacts acquired from a fingertip in the public healthcare database named Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) by using 11 features as the input of the random forest algorithm and classified the signals into two classes: acceptable and anomalous. A real-time algorithm is proposed to identify artifacts by using the method. The efficient Fisher score feature selection algorithm was used to order and select 11 relevant features from 19 available features that represented the PPG signal very effectively. Six machine learning algorithms (random forest, decision tree, Gaussian naïve Bayes, linear support vector machine, artificial neural network, and probabilistic neural network) were applied with the extracted features, and their classification accuracy was measured. Among them, the random forest had the best performance using only 11 out of 19 features with an accuracy of 85.68%. Our proposed method also achieved good sensitivity and specificity value of 86.57% and 85.09%, respectively. The proposed real-time algorithm can be an easy and convenient way for real-time PPG signal artifact detection using smartphones and wearable devices.

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A Novel Personalized Motion and Noise Artifact (MNA) Detection Method for Smartphone Photoplethysmograph (PPG) Signals

Cited by 30 publications

References 72 publications

A novel diversity method for smartphone camera-based heart rhythm signals in the presence of motion and noise artifacts

A novel diversity method for smartphone camera-based heart rhythm signals in the presence of motion and noise artifacts

A Noninvasive Blood Glucose Monitoring System Based on Smartphone PPG Signal Processing and Machine Learning

An Efficient Fingertip Photoplethysmographic Signal Artifact Detection Method: A Machine Learning Approach

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