Sensor Agnostic Photoplethysmogram Signal Quality Assessment using Morphological Analysis

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.

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