UMass Chan Medical School Faculty Publications


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

UMMS Affiliation

Division of Cardiovascular Medicine, Department of Medicine

Publication Date


Document Type



Analytical, Diagnostic and Therapeutic Techniques and Equipment | Biomedical Engineering and Bioengineering | Cardiology | Health Information Technology | Medical Biotechnology


Photoplethysmography (PPG) is a technique to detect blood volume changes in an optical way. Representative PPG applications are the measurements of oxygen saturation, heart rate, and respiratory rate. However, PPG signals are sensitive to motion and noise artifacts (MNAs) especially when they are obtained from smartphone cameras. Moreover, PPG signals are different among users and each individual's PPG signal has a unique characteristic. Hence, an effective MNA detection and reduction method for smartphone PPG signals, which adapts itself to each user in a personalized way, is highly demanded. Here, a concept of the probabilistic neural network (PNN) is introduced to be used with the proposed extracted parameters. The signal amplitude, standard deviation of peak to peak time intervals and amplitudes, along with the mean of moving standard deviation, signal slope changes, and the optimal autoregressive (AR) model order are proposed for effective MNA detection. Accordingly, the performance of the proposed personalized algorithm is compared with conventional MNA detection algorithms. As performance metrics, we considered accuracy, sensitivity, and specificity. The results show that the overall performance of the personalized MNA detection is enhanced compared to the generalized algorithm. The average values of the accuracy, sensitivity and specificity of the personalized one are 98.07%, 92.6%, and 99.78%, respectively, while these are 89.92%, 84.21%, and 93.63% for the general one.


Personalization, Photoplethysmography, PPG, Motion Noise Artifacts, Signal Quality Index

DOI of Published Version



Tabei F, Kumar R, Phan TN, McManus DD, Chong JW. A Novel Personalized Motion and Noise Artifact (MNA) Detection Method for Smartphone Photoplethysmograph (PPG) Signals. IEEE Access. 2018;6:60498-60512. doi: 10.1109/ACCESS.2018.2875873. Epub 2018 Oct 16. PMID: 31263653; PMCID: PMC6602087. Link to article on publisher's site

Related Resources

Link to Article in PubMed

Journal/Book/Conference Title

IEEE access : practical innovations, open solutions

PubMed ID