A Novel Real-Time Non-invasive Hemoglobin Level Detection Using Video Images from Smartphone Camera

Ahsan, Golam Mushih Tanimul; Gani, Md. Osman; Hasan, Md. Kamrul; Ahamed, Sheikh Iqbal; Chu, William Cheng‐Chung; Adibuzzaman, Mohammad; Field, Joshua J.

doi:10.1109/compsac.2017.29

Cited by 14 publications

(9 citation statements)

References 15 publications

(6 reference statements)

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“…Linear regression is applied on PPG signals obtained by averaging the pixel intensities from 1 minute long video. [2] study PPG signals along with some other statistical information such as median, mode, maximum, minimum, and standard deviation for each Red, Green, and Blue channels to estimate the hemoglobin level. Singular value decomposition (SVD) is used to remove the insignificant data for each frame.…”

Section: B Ppg Signals and Machine Learning Based Solutionsmentioning

confidence: 99%

“…Considering its severity, Hb level estimation is necessary not only for anemic disorder but also for assessing the response of iron supplement treatments [43], [44], treatment of dengue fever [23], blood transfusion [4], and for the diagnoses of childhood malnutrition. For this purpose, laboratory test [2] is most common procedure which accurately estimates Hb level however, at the cost of time and money for a patient and significant financial investment for laboratory equipment.…”

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confidence: 99%

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Fingertip Video Dataset for Non-Invasive Diagnosis of Anemia Using ResNet-18 Classifier

Sabir,

Khan,

Ishaq

et al. 2024

IEEE Access

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Hemoglobin is the iron containing protein in red blood cells which carries oxygen from lungs to rest of the body tissues.Accurate measurement of hemoglobin is essential for diagnosing anemia, a condition characterized by a deficiency of red blood cells. This measurement is particularly vital before initiating blood transfusions for thalassemia patients.Non-invasive estimation of hemoglobin levels can be achieved through photoplethysmography (PPG)-based methods.PPG is an optical method to measure blood volume changes in successive heart beats. PPG signals can be obtained from fingertip videos using a light source and a photodetector. SmartphonePPG utilizes a smartphone's flashlight as a light source and its camera as a photodetector to acquire PPG signals, offering an affordable and portable point-ofcare tool. Despite the ubiquity of smartphones, signals from their cameras often contain noise, making feature selection from PPG characteristics challenging. While PPG-based methods are invaluable, the lack of real-world datasets poses a significant challenge in maximizing the benefits of PPG technology. In this paper, we introduce a dataset comprising 1-minute fingertip video recordings from 150 anemic patients, obtained using a smartphone's camera. The dataset, publicly accessible for research purposes a , covers an age range of 6 months to 32 years, with diverse hemoglobin values (4.3 gm/dL -12.4 gm/dL).Utilizing this dataset, we propose a deep learning-based technique employing the ResNet-18 architecture to estimate hemoglobin levels. This approach eliminates the need for manual feature extraction and selection from PPG signals, overcoming a limitation in existing smartphonePPG-based hemoglobin estimation systems. Our model achieves a hemoglobin level estimation with an RMSE of 0.81-1.39 when compared with the gold standard laboratory method, Complete Blood Count (CBC) test reports.In contrast, HemaApp, a state-ofthe-art research utilizing a machine learning-based classifier (SVM), yields an RMSE of 1.7 on our dataset. The accuracy and simplicity of our model position it as a promising alternative to existing non-invasive hemoglobin level estimation methods. a https://forms.gle/LB4qn81ZMqEuy3V27

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Section: B Ppg Signals and Machine Learning Based Solutionsmentioning

confidence: 99%

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confidence: 99%

Fingertip Video Dataset for Non-Invasive Diagnosis of Anemia Using ResNet-18 Classifier

Sabir,

Khan,

Ishaq

et al. 2024

IEEE Access

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“…The dataset considered is very small, consisting of only five patients with sickle cell anemia, but this number seems significant for the study carried out. The patient sample considered has been considerably expanded and a new experiment has been presented by the same authors in [49]: people from different areas of the world have been taken into consideration to try to outline a general model In [39], a comparison is made between images taken under ambient light conditions, using a digital camera and a smartphone camera. The authors use a method of standardizing the color of the image with a color calibration card, however, they emphasize that the effect of ambient lighting is not completely eliminated.…”

Section: Estimation Of Anemia Through Fingertip Analysismentioning

confidence: 99%

“…The dataset considered is very small, consisting of only five patients with sickle cell anemia, but this number seems significant for the study carried out. The patient sample considered has been considerably expanded and a new experiment has been presented by the same authors in [49]: people from different areas of the world have been taken into consideration to try to outline a general model that allows to better estimate the anemia. The same authors, in [50] present a prediction model of the hemoglobin level which demonstrates an accuracy of 95%.…”

Section: Estimation Of Anemia Through Fingertip Analysismentioning

confidence: 99%

Estimate of Anemia with New Non-Invasive Systems—A Moment of Reflection

et al. 2020

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Anemia is a global public health problem with major consequences for human health. About a quarter of the world population shows a hemoglobin concentration that is below the recommended thresholds. Non-invasive methods for monitoring and identifying potential risk of anemia and smartphone-based devices to perform this task are promising in addressing this pathology. We have considered some well-known studies carried out on this topic since the main purpose of this work was not to produce a review. The first group of papers describes the approaches for the clinical evaluation of anemia focused on different human exposed tissues, while we used a second group to overview some technologies, basic methods, and principles of operation of some devices and highlight some technical problems. Results extracted from the second group of papers examined were aggregated in two comparison tables. A growing interest in this topic is demonstrated by the increasing number of papers published recently. We believe we have identified several critical issues in the published studies, including those published by us. Just as an example, in many papers the dataset used is not described. With this paper we wish to open a discussion on these issues. Few papers have been sufficient to highlight differences in the experimental conditions and this makes the comparison of the results difficult. Differences are also found in the identification of the regions of interest in the tissue, descriptions of the datasets, and other boundary conditions. These critical issues are discussed together with open problems and common mistakes that probably we are making. We propose the definition of a road-map and a common agenda for research on this topic. In this sense, we want to highlight here some issues that seem worthy of common discussion and the subject of synergistic agreements. This paper, and in particular, the discussion could be the starting point for an open debate about the dissemination of our experiments and pave the way for further updates and improvements of what we have outlined.

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“…In this method, video images collected from human fingertips are used for non-invasive hemoglobin prediction. Although the basic hypothesis of this method works, it must be combined with more effective methods for clinical application [ 13 ]. Kesarwani et al developed a non-invasive anemia detection system by combining current state-of-the-art computational methods with observations of palmar pallor.…”

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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A Novel Real-Time Non-invasive Hemoglobin Level Detection Using Video Images from Smartphone Camera

Cited by 14 publications

References 15 publications

Fingertip Video Dataset for Non-Invasive Diagnosis of Anemia Using ResNet-18 Classifier

Fingertip Video Dataset for Non-Invasive Diagnosis of Anemia Using ResNet-18 Classifier

Estimate of Anemia with New Non-Invasive Systems—A Moment of Reflection

A Non-Invasive Hemoglobin Detection Device Based on Multispectral Photoplethysmography

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