Research Article
Sensor Agnostic Photoplethysmogram Signal Quality Assessment using Morphological Analysis
@INPROCEEDINGS{10.4108/eai.7-11-2017.2273905, author={Shahnawaz Alam and Shreyasi Datta and Anirban Dutta Choudhury and Arpan Pal}, title={Sensor Agnostic Photoplethysmogram Signal Quality Assessment using Morphological Analysis}, proceedings={14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services}, publisher={ACM}, proceedings_a={MOBIQUITOUS}, year={2018}, month={4}, keywords={photoplethysmogram artefact morphology wearable sensors noise detection mobile health}, doi={10.4108/eai.7-11-2017.2273905} }- Shahnawaz Alam
Shreyasi Datta
Anirban Dutta Choudhury
Arpan Pal
Year: 2018
Sensor Agnostic Photoplethysmogram Signal Quality Assessment using Morphological Analysis
MOBIQUITOUS
ACM
DOI: 10.4108/eai.7-11-2017.2273905
Abstract
In this article, we propose a method to assess the clinical usability of fingertip Photoplethysmogram (PPG) waveform, collected from medical grade oximeter (train data) and smartphone (test data). We introduce a set of novel Signal Quality Indices (SQIs) to represent the noise characteristics of the PPG waveform. The SQIs are presented to a random forest classifier to discriminate between clean and noisy signals. The proposed method was evaluated on datasets annotated by four experts, resulting into a sensitivity and specificity of (92 +− 4.7 % , 95 +− 3 %) and (82.6 +− 4.6 % , 95.4 +− 3.1 %) on train and test data respectively. Further we applied the proposed method on PPG waveform of clinically proven control and disease population of Coronary Artery Disease (CAD), which resulted into (77 %,77 %) of sensitivity and specificity respectively.
