Assessment of Photoplethysmography Method in Extraction of Hemoglobin Concentration

Feasibility and accuracy of noninvasive continuous hemoglobin monitoring using transesophageal photoplethysmography in porcine model

BACKGROUND

Continuous and noninvasive hemoglobin (Hb) monitoring during surgery is essential for anesthesiologists to make transfusions decisions. The aim of this study was to investigate the feasibility and accuracy of noninvasive and continuous Hb monitoring using transesophageal descending aortic photoplethysmography (dPPG) in porcine model.

METHODS

Nineteen landrace pigs, aged 3 to 5 months and weighing 30 to 50 kg, were enrolled in this study. A homemade oximetry sensor, including red (660 nm) and infrared (940 nm) lights, was placed in the esophagus for dPPG signal detection to pair with the corresponding reference Hb values (Hbi-STAT) measured by blood gas analysis. The decrease and increase changes in Hb concentration were achieved by hemodilution and transfusion. Metrics, including alternating current (AC), direct current (DC), and AC/DC for both red and infrared light were extracted from the dPPG signal. A receiver operating characteristic (ROC) curve was built to evaluate the performance of dPPG metrics in predicting the Hb "trigger threshold" of transfusion (Hb < 60 g/L and Hb > 100 g/L). Agreement and trending ability between Hb measured by dPPG (HbdPPG) and by blood gas analysis were analyzed by Bland-Altman method and polar plot graph. Error grid analysis was also performed to evaluate clinical significance of HbdPPG measurement.

RESULTS

The dPPG signal was successfully detected in all of the enrolled experimental pigs, without the occurrence of a continuous loss of dPPG signal for 2 min during the entire measurement. A total of 376 pairs of dPPG signal and Hbi-STAT were acquired. ACred/DCred and ACinf/DCinf had moderate correlations with Hbi-STAT, and the correlation coefficients were 0.790 and 0.782, respectively. The areas under the ROC curve for ACred/DCred and ACinf/DCinf in predicting Hbi-STAT < 60 g/L were 0.85 and 0.75, in predicting Hbi-STAT > 100 g/L were 0.90 and 0.83, respectively. Bland-Altman analysis and polar plot showed a small bias (1.69 g/L) but a wide limit of agreement (-26.02-29.40 g/L) and a poor trend ability between HbdPPG and Hbi-STAT. Clinical significance analysis showed that 82% of the data lay within the Zone A, 18% within the Zone B, and 0% within the Zone C.

CONCLUSION

It is feasible to establish a noninvasive and continuous Hb monitoring by transesophageal dPPG signal. The ACred/DCred extracted from the dPPG signal could provide a sensitive prediction of the Hb threshold for transfusion. The Hb concentration measured by dPPG signal has a moderate correlation with that measured by blood gas analysis. This animal study may provide an experimental basis for the development of bedside HbdPPG monitoring in the future.

A dataset of hemoglobin blood value and photoplethysmography signal for machine learning-based non-invasive hemoglobin measurement

Hemoglobin (Hb), a protein found within red blood cells, is responsible for transporting oxygen and carbon dioxide gasses. A low concentration of Hb indicates the existence of anemia. Traditional invasive Hb examination methods are accurate but have drawbacks, such as pain. A new approach, non-invasive photoplethysmography (PPG), addresses these issues and allows real-time Hb examination. In this article, the dataset consists of PPG signal, gender, age, and Hb value. The PPG signal was measured by a MAX30102 module sensor that emitted two types of light (red and infra-red light) and measured using a photodetector. Total of 68 subjects (56% female and 44% male) within the age of 18-65 years were collected. The total dataset is 816 data from 68 subjects, which each subject provides 12 sets of red and infra-red light signals. The data were collected at Primary Health Center Jatiuwung, Tangerang City, Banten 15,138, Indonesia. Researchers interested in anemia monitoring and those pursuing the development of non-invasive hemoglobin measurement based on machine learning can leverage this dataset.

Emerging point-of-care technologies for anemia detection

Anemia, characterized by low blood hemoglobin level, affects about 25% of the world's population with the heaviest burden borne by women and children. Anemia leads to impaired cognitive development in children, as well as high morbidity and early mortality among sufferers. Anemia can be caused by nutritional deficiencies, oncologic treatments and diseases, and infections such as malaria, as well as inherited hemoglobin or red cell disorders. Effective treatments are available for anemia upon early detection and the treatment method is highly dependent on the cause of anemia. There is a need for point-of-care (POC) screening, early diagnosis, and monitoring of anemia, which is currently not widely accessible due to technical challenges and cost, especially in low- and middle-income countries where anemia is most prevalent. This review first introduces the evolution of anemia detection methods followed by their implementation in current commercially available POC anemia diagnostic devices. Then, emerging POC anemia detection technologies leveraging new methods are reviewed. Finally, we highlight the future trends of integrating anemia detection with the diagnosis of relevant underlying disorders to accurately identify specific root causes and to facilitate personalized treatment and care.