Non-invasive anaemia detection by examining palm pallor: A smartphone-based approach

Kesarwani, Abhishek; Das, Sunanda; Dalui, Mamata; Kisku, Dakshina Ranjan; Sen, Bibhash; Roy, Suchismita; Basu, Anupam

doi:10.1016/j.bspc.2022.104045

Cited by 8 publications

(1 citation statement)

References 29 publications

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“…Kesarwani et al developed a non-invasive anemia detection system by combining current state-of-the-art computational methods with observations of palmar pallor. The proposed system ensured 93% sensitivity with Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) values of 0.701g/dL and 0.698 g/dL, respectively [ 14 ]. However, its accuracy still needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

A Non-Invasive Hemoglobin Detection Device Based on Multispectral Photoplethysmography

Zhu,

Sun,

Liu

et al. 2023

Biosensors

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The measurement of hemoglobin is a vital index for diagnosing and monitoring diseases in clinical practice. At present, solutions need to be found for the soreness, high risk of infection, and inconvenient operation associated with invasive detection methods. This paper proposes a method for non-invasively detecting hemoglobin levels based on multi-wavelength photoplethysmography (PPG) signals. AFE4490 and TMUX1109 were used to implement the low-cost collection of an eight-LED transmissive PPG signal. We used seven regular LEDs and one broadband LED (Osram SFH4737) as light sources. Additionally, a finger clip integrating multiple sensors was designed and manufactured via 3D printing to simultaneously monitor the LED–sensor distance and the pressure from the tester’s finger during PPG signal acquisition. We used a method to extract features from PPG signals using a sliding-window’s variance and an evaluation metric for PPG signals based on the AdaCost classification. Data were gathered from 56 participants from the Nephrology department, including 16 anemic patients. Pearson correlation analysis was conducted on the collected data to remove any data with a weak correlation. The advantage of using a broadband LED as a light source was also demonstrated. Several non-invasive hemoglobin regression models were created by applying AdaBoost, BPNN, and Random Forest models. The study’s results indicate that the AdaBoost model produced the best performance, with a mean absolute error (MAE) of 2.67 g/L and a correlation coefficient (R2) of 0.91 The study results show that the device we designed and manufactured can achieve effective non-invasive hemoglobin detection and represents a new methodological approach to obtaining measurements that can be applied in a clinical setting.

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Section: Introductionmentioning

confidence: 99%