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Two billion people are affected by hemoglobin (Hgb) related diseases. Usual clinical assessments of Hgb are conducted by analyzing venipuncture-obtained blood samples in laboratories. A non-invasive, cheap, point-of-care and accurate Hgb test is needed everywhere. Our group has developed a non-invasive Hgb measurement system using 10-second Smartphone videos of the index fingertips. Custom hardware sets were used to illuminate the fingers. We tested four lighting conditions with wavelengths in the near-infrared spectrum suggested by the absorption properties of two primary components of blood-oxygenated Hgb and plasma. We found a strong linear correlation between our measured and laboratory-measured Hgb levels in 167 patients with a mean absolute percentage error (MAPE) of 5%. In our initial analysis, critical tasks were performed manually. Now, using the same data, we have automated or modified all the steps. For all, male, and female subjects we found a MAPE of 6.43%, 5.34%, and 4.85 and mean squared error (MSE) of 0.84, 0.5, and 0.49 respectively. The new analyses however, have suggested inexplicable inconsistencies in our results, which we attribute to laboratory measurement errors reflected in a non-normative distribution of Hgb levels in our studied patients, as well as excess noise in the specific signals we measured in the videos. Based on these encouraging results, and the promise of greater accuracy with our revised hardware and software tools, we now propose a rigorous validation study to demonstrate that this approach to hemoglobin measurement is appropriate for general clinical application.

ieee journal of biomedical and health informatics

Automated Cardiac Pulse Cycle Analysis From Photoplethysmogram (PPG) Signals Generated From Fingertip Videos Captured Using a Smartphone to Measure Blood Hemoglobin Levels