Motion and Noise Artifact Detection in Smartphone Photoplethysmograph Signals Using Personalized Classifier

Health parameters such as heart rhythm, blood pressure, and the level of oxygen saturation in the blood could be measured with photoplethysmography (PPG) signal. The advent of smartphone camera sensors has enabled the extraction of PPG signals from smartphones. PPG signals are weak at motion and noise artifacts (MNA) which could generate unreliable heart rate measurement. Smartphone PPG signals are more prone to MNA since they are not designed for clinical applications. PPG signals are known as biometric signals since they have unique behaviors for each individual. However, in previous MNA detection studies this personalized characteristic has not been considered. In this paper, we propose a novel personalized MNA detection method by applying a probabilistic neural network as a classifier. The performance of our personalized method is evaluated with 25 volunteered subjects in terms of accuracy, specificity, and sensitivity and compared with the generalized method. The average accuracy of our personalized method is 97.96% while it is 92.94% in the generalized one. The average values of personalized specificity and sensitivity are 99.69% and 93.91% while the generalized classifier gives 95.38% and 87.4%.